The aim of this paper is to broaden the application of Stochastic Configuration Network (SCN) in the semi-supervised domain by utilizing common unlabeled data in daily life. It can enhance the classification accuracy ...The aim of this paper is to broaden the application of Stochastic Configuration Network (SCN) in the semi-supervised domain by utilizing common unlabeled data in daily life. It can enhance the classification accuracy of decentralized SCN algorithms while effectively protecting user privacy. To this end, we propose a decentralized semi-supervised learning algorithm for SCN, called DMT-SCN, which introduces teacher and student models by combining the idea of consistency regularization to improve the response speed of model iterations. In order to reduce the possible negative impact of unsupervised data on the model, we purposely change the way of adding noise to the unlabeled data. Simulation results show that the algorithm can effectively utilize unlabeled data to improve the classification accuracy of SCN training and is robust under different ground simulation environments.展开更多
Radio frequency fingerprinting(RFF)is a remarkable lightweight authentication scheme to support rapid and scalable identification in the internet of things(IoT)systems.Deep learning(DL)is a critical enabler of RFF ide...Radio frequency fingerprinting(RFF)is a remarkable lightweight authentication scheme to support rapid and scalable identification in the internet of things(IoT)systems.Deep learning(DL)is a critical enabler of RFF identification by leveraging the hardware-level features.However,traditional supervised learning methods require huge labeled training samples.Therefore,how to establish a highperformance supervised learning model with few labels under practical application is still challenging.To address this issue,we in this paper propose a novel RFF semi-supervised learning(RFFSSL)model which can obtain a better performance with few meta labels.Specifically,the proposed RFFSSL model is constituted by a teacher-student network,in which the student network learns from the pseudo label predicted by the teacher.Then,the output of the student model will be exploited to improve the performance of teacher among the labeled data.Furthermore,a comprehensive evaluation on the accuracy is conducted.We derive about 50 GB real long-term evolution(LTE)mobile phone’s raw signal datasets,which is used to evaluate various models.Experimental results demonstrate that the proposed RFFSSL scheme can achieve up to 97%experimental testing accuracy over a noisy environment only with 10%labeled samples when training samples equal to 2700.展开更多
Intrusion detection involves identifying unauthorized network activity and recognizing whether the data constitute an abnormal network transmission.Recent research has focused on using semi-supervised learning mechani...Intrusion detection involves identifying unauthorized network activity and recognizing whether the data constitute an abnormal network transmission.Recent research has focused on using semi-supervised learning mechanisms to identify abnormal network traffic to deal with labeled and unlabeled data in the industry.However,real-time training and classifying network traffic pose challenges,as they can lead to the degradation of the overall dataset and difficulties preventing attacks.Additionally,existing semi-supervised learning research might need to analyze the experimental results comprehensively.This paper proposes XA-GANomaly,a novel technique for explainable adaptive semi-supervised learning using GANomaly,an image anomalous detection model that dynamically trains small subsets to these issues.First,this research introduces a deep neural network(DNN)-based GANomaly for semi-supervised learning.Second,this paper presents the proposed adaptive algorithm for the DNN-based GANomaly,which is validated with four subsets of the adaptive dataset.Finally,this study demonstrates a monitoring system that incorporates three explainable techniques—Shapley additive explanations,reconstruction error visualization,and t-distributed stochastic neighbor embedding—to respond effectively to attacks on traffic data at each feature engineering stage,semi-supervised learning,and adaptive learning.Compared to other single-class classification techniques,the proposed DNN-based GANomaly achieves higher scores for Network Security Laboratory-Knowledge Discovery in Databases and UNSW-NB15 datasets at 13%and 8%of F1 scores and 4.17%and 11.51%for accuracy,respectively.Furthermore,experiments of the proposed adaptive learning reveal mostly improved results over the initial values.An analysis and monitoring system based on the combination of the three explainable methodologies is also described.Thus,the proposed method has the potential advantages to be applied in practical industry,and future research will explore handling unbalanced real-time datasets in various scenarios.展开更多
Malaria is a lethal disease responsible for thousands of deaths worldwide every year.Manual methods of malaria diagnosis are timeconsuming that require a great deal of human expertise and efforts.Computerbased automat...Malaria is a lethal disease responsible for thousands of deaths worldwide every year.Manual methods of malaria diagnosis are timeconsuming that require a great deal of human expertise and efforts.Computerbased automated diagnosis of diseases is progressively becoming popular.Although deep learning models show high performance in the medical field,it demands a large volume of data for training which is hard to acquire for medical problems.Similarly,labeling of medical images can be done with the help of medical experts only.Several recent studies have utilized deep learning models to develop efficient malaria diagnostic system,which showed promising results.However,the most common problem with these models is that they need a large amount of data for training.This paper presents a computer-aided malaria diagnosis system that combines a semi-supervised generative adversarial network and transfer learning.The proposed model is trained in a semi-supervised manner and requires less training data than conventional deep learning models.Performance of the proposed model is evaluated on a publicly available dataset of blood smear images(with malariainfected and normal class)and achieved a classification accuracy of 96.6%.展开更多
Through semi-supervised learning and knowledge inheritance,a novel Takagi-Sugeno-Kang(TSK)fuzzy system framework is proposed for epilepsy data classification in this study.The new method is based on the maximum mean d...Through semi-supervised learning and knowledge inheritance,a novel Takagi-Sugeno-Kang(TSK)fuzzy system framework is proposed for epilepsy data classification in this study.The new method is based on the maximum mean discrepancy(MMD)method and TSK fuzzy system,as a basic model for the classification of epilepsy data.First,formedical data,the interpretability of TSK fuzzy systems can ensure that the prediction results are traceable and safe.Second,in view of the deviation in the data distribution between the real source domain and the target domain,MMD is used to measure the distance between different data distributions.The objective function is constructed according to the MMD distance,and the distribution distance of different datasets is minimized to find the similar characteristics of different datasets.We introduce semi-supervised learning to further explore the relationship between data.Based on the MMD method,a semi-supervised learning(SSL)-MMD method is constructed by using pseudo-tags to realize the data distribution alignment of the same category.In addition,the idea of knowledge dissemination is used to learn pseudo-tags as additional data features.Finally,for epilepsy classification,the cross-domain TSK fuzzy system uses the cross-entropy function as the objective function and adopts the back-propagation strategy to optimize the parameters.The experimental results show that the new method can process complex epilepsy data and identify whether patients have epilepsy.展开更多
In the upcoming large-scale Internet of Things(Io T),it is increasingly challenging to defend against malicious traffic,due to the heterogeneity of Io T devices and the diversity of Io T communication protocols.In thi...In the upcoming large-scale Internet of Things(Io T),it is increasingly challenging to defend against malicious traffic,due to the heterogeneity of Io T devices and the diversity of Io T communication protocols.In this paper,we propose a semi-supervised learning-based approach to detect malicious traffic at the access side.It overcomes the resource-bottleneck problem of traditional malicious traffic defenders which are deployed at the victim side,and also is free of labeled traffic data in model training.Specifically,we design a coarse-grained behavior model of Io T devices by self-supervised learning with unlabeled traffic data.Then,we fine-tune this model to improve its accuracy in malicious traffic detection by adopting a transfer learning method using a small amount of labeled data.Experimental results show that our method can achieve the accuracy of 99.52%and the F1-score of 99.52%with only 1%of the labeled training data based on the CICDDoS2019 dataset.Moreover,our method outperforms the stateof-the-art supervised learning-based methods in terms of accuracy,precision,recall and F1-score with 1%of the training data.展开更多
Sentiment classification is a useful tool to classify reviews about sentiments and attitudes towards a product or service.Existing studies heavily rely on sentiment classification methods that require fully annotated ...Sentiment classification is a useful tool to classify reviews about sentiments and attitudes towards a product or service.Existing studies heavily rely on sentiment classification methods that require fully annotated inputs.However,there is limited labelled text available,making the acquirement process of the fully annotated input costly and labour-intensive.Lately,semi-supervised methods emerge as they require only partially labelled input but perform comparably to supervised methods.Nevertheless,some works reported that the performance of the semi-supervised model degraded after adding unlabelled instances into training.Literature also shows that not all unlabelled instances are equally useful;thus identifying the informative unlabelled instances is beneficial in training a semi-supervised model.To achieve this,an informative score is proposed and incorporated into semisupervised sentiment classification.The evaluation is performed on a semisupervised method without an informative score and with an informative score.By using the informative score in the instance selection strategy to identify informative unlabelled instances,semi-supervised models perform better compared to models that do not incorporate informative scores into their training.Although the performance of semi-supervised models incorporated with an informative score is not able to surpass the supervised models,the results are still found promising as the differences in performance are subtle with a small difference of 2%to 5%,but the number of labelled instances used is greatly reduced from100%to 40%.The best finding of the proposed instance selection strategy is achieved when incorporating an informative score with a baseline confidence score at a 0.5:0.5 ratio using only 40%labelled data.展开更多
Label propagation is an essential semi-supervised learning method based on graphs,which has a broad spectrum of applications in pattern recognition and data mining.This paper proposes a quantum semi-supervised classif...Label propagation is an essential semi-supervised learning method based on graphs,which has a broad spectrum of applications in pattern recognition and data mining.This paper proposes a quantum semi-supervised classifier based on label propagation.Considering the difficulty of graph construction,we develop a variational quantum label propagation(VQLP)method.In this method,a locally parameterized quantum circuit is created to reduce the parameters required in the optimization.Furthermore,we design a quantum semi-supervised binary classifier based on hybrid Bell and Z bases measurement,which has a shallower circuit depth and is more suitable for implementation on near-term quantum devices.We demonstrate the performance of the quantum semi-supervised classifier on the Iris data set,and the simulation results show that the quantum semi-supervised classifier has higher classification accuracy than the swap test classifier.This work opens a new path to quantum machine learning based on graphs.展开更多
Recent state-of-the-art semi-supervised learning(SSL)methods usually use data augmentations as core components.Such methods,however,are limited to simple transformations such as the augmentations under the instance’s...Recent state-of-the-art semi-supervised learning(SSL)methods usually use data augmentations as core components.Such methods,however,are limited to simple transformations such as the augmentations under the instance’s naive representations or the augmentations under the instance’s semantic representations.To tackle this problem,we offer a unique insight into data augmentations and propose a novel data-augmentation-based semi-supervised learning method,called Attentive Neighborhood Feature Aug-mentation(ANFA).The motivation of our method lies in the observation that the relationship between the given feature and its neighborhood may contribute to constructing more reliable transformations for the data,and further facilitating the classifier to distinguish the ambiguous features from the low-dense regions.Specially,we first project the labeled and unlabeled data points into an embedding space and then construct a neighbor graph that serves as a similarity measure based on the similar representations in the embedding space.Then,we employ an attention mechanism to transform the target features into augmented ones based on the neighbor graph.Finally,we formulate a novel semi-supervised loss by encouraging the predictions of the interpolations of augmented features to be consistent with the corresponding interpolations of the predictions of the target features.We carried out exper-iments on SVHN and CIFAR-10 benchmark datasets and the experimental results demonstrate that our method outperforms the state-of-the-art methods when the number of labeled examples is limited.展开更多
Clustering is a crucial method for deciphering data structure and producing new information.Due to its significance in revealing fundamental connections between the human brain and events,it is essential to utilize cl...Clustering is a crucial method for deciphering data structure and producing new information.Due to its significance in revealing fundamental connections between the human brain and events,it is essential to utilize clustering for cognitive research.Dealing with noisy data caused by inaccurate synthesis from several sources or misleading data production processes is one of the most intriguing clustering difficulties.Noisy data can lead to incorrect object recognition and inference.This research aims to innovate a novel clustering approach,named Picture-Neutrosophic Trusted Safe Semi-Supervised Fuzzy Clustering(PNTS3FCM),to solve the clustering problem with noisy data using neutral and refusal degrees in the definition of Picture Fuzzy Set(PFS)and Neutrosophic Set(NS).Our contribution is to propose a new optimization model with four essential components:clustering,outlier removal,safe semi-supervised fuzzy clustering and partitioning with labeled and unlabeled data.The effectiveness and flexibility of the proposed technique are estimated and compared with the state-of-art methods,standard Picture fuzzy clustering(FC-PFS)and Confidence-weighted safe semi-supervised clustering(CS3FCM)on benchmark UCI datasets.The experimental results show that our method is better at least 10/15 datasets than the compared methods in terms of clustering quality and computational time.展开更多
Clustering analysis is one of the main concerns in data mining.A common approach to the clustering process is to bring together points that are close to each other and separate points that are away from each other.The...Clustering analysis is one of the main concerns in data mining.A common approach to the clustering process is to bring together points that are close to each other and separate points that are away from each other.Therefore,measuring the distance between sample points is crucial to the effectiveness of clustering.Filtering features by label information and mea-suring the distance between samples by these features is a common supervised learning method to reconstruct distance metric.However,in many application scenarios,it is very expensive to obtain a large number of labeled samples.In this paper,to solve the clustering problem in the few supervised sample and high data dimensionality scenarios,a novel semi-supervised clustering algorithm is proposed by designing an improved prototype network that attempts to reconstruct the distance metric in the sample space with a small amount of pairwise supervised information,such as Must-Link and Cannot-Link,and then cluster the data in the new metric space.The core idea is to make the similar ones closer and the dissimilar ones further away through embedding mapping.Extensive experiments on both real-world and synthetic datasets show the effectiveness of this algorithm.Average clustering metrics on various datasets improved by 8%compared to the comparison algorithm.展开更多
Deep Learning(DL)is such a powerful tool that we have seen tremendous success in areas such as Computer Vision,Speech Recognition,and Natural Language Processing.Since Automated Modulation Classification(AMC)is an imp...Deep Learning(DL)is such a powerful tool that we have seen tremendous success in areas such as Computer Vision,Speech Recognition,and Natural Language Processing.Since Automated Modulation Classification(AMC)is an important part in Cognitive Radio Networks,we try to explore its potential in solving signal modulation recognition problem.It cannot be overlooked that DL model is a complex model,thus making them prone to over-fitting.DL model requires many training data to combat with over-fitting,but adding high quality labels to training data manually is not always cheap and accessible,especially in real-time system,which may counter unprecedented data in dataset.Semi-supervised Learning is a way to exploit unlabeled data effectively to reduce over-fitting in DL.In this paper,we extend Generative Adversarial Networks(GANs)to the semi-supervised learning will show it is a method can be used to create a more dataefficient classifier.展开更多
A Machine Learning (ML)-based Intrusion Detection and Prevention System (IDPS)requires a large amount of labeled up-to-date training data to effectively detect intrusions and generalize well to novel attacks.However,t...A Machine Learning (ML)-based Intrusion Detection and Prevention System (IDPS)requires a large amount of labeled up-to-date training data to effectively detect intrusions and generalize well to novel attacks.However,the labeling of data is costly and becomes infeasible when dealing with big data,such as those generated by Intemet of Things applications.To this effect,building an ML model that learns from non-labeled or partially labeled data is of critical importance.This paper proposes a Semi-supervised Mniti-Layered Clustering ((SMLC))model for the detection and prevention of network intrusion.SMLC has the capability to learn from partially labeled data while achieving a detection performance comparable to that of supervised ML-based IDPS.The performance of SMLC is compared with that of a well-known semi-supervised model (tri-training)and of supervised ensemble ML models, namely Random.Forest,Bagging,and AdaboostM1on two benchmark network-intrusion datasets,NSL and Kyoto 2006+.Experimental resnits show that SMLC is superior to tri-training,providing a comparable detection accuracy with 20%less labeled instances of training data.Furthermore,our results demonstrate that our scheme has a detection accuracy comparable to that of the supervised ensemble models.展开更多
The majority of big data analytics applied to transportation datasets suffer from being too domain-specific,that is,they draw conclusions for a dataset based on analytics on the same dataset.This makes models trained ...The majority of big data analytics applied to transportation datasets suffer from being too domain-specific,that is,they draw conclusions for a dataset based on analytics on the same dataset.This makes models trained from one domain(e.g.taxi data)applies badly to a different domain(e.g.Uber data).To achieve accurate analyses on a new domain,substantial amounts of data must be available,which limits practical applications.To remedy this,we propose to use semi-supervised and active learning of big data to accomplish the domain adaptation task:Selectively choosing a small amount of datapoints from a new domain while achieving comparable performances to using all the datapoints.We choose the New York City(NYC)transportation data of taxi and Uber as our dataset,simulating different domains with 90%as the source data domain for training and the remaining 10%as the target data domain for evaluation.We propose semi-supervised and active learning strategies and apply it to the source domain for selecting datapoints.Experimental results show that our adaptation achieves a comparable performance of using all datapoints while using only a fraction of them,substantially reducing the amount of data required.Our approach has two major advantages:It can make accurate analytics and predictions when big datasets are not available,and even if big datasets are available,our approach chooses the most informative datapoints out of the dataset,making the process much more efficient without having to process huge amounts of data.展开更多
It is crucial to maintain the safe and stable operation of distribution transformers,which constitute a key part of power systems.In the event of transformer failure,the fault type must be diagnosed in a timely and ac...It is crucial to maintain the safe and stable operation of distribution transformers,which constitute a key part of power systems.In the event of transformer failure,the fault type must be diagnosed in a timely and accurate manner.To this end,a transformer fault diagnosis method based on infrared image processing and semi-supervised learning is proposed herein.First,we perform feature extraction on the collected infrared-image data to extract temperature,texture,and shape features as the model reference vectors.Then,a generative adversarial network(GAN)is constructed to generate synthetic samples for the minority subset of labelled samples.The proposed method can learn information from unlabeled sample data,unlike conventional supervised learning methods.Subsequently,a semi-supervised graph model is trained on the entire dataset,i.e.,both labeled and unlabeled data.Finally,we test the proposed model on an actual dataset collected from a Chinese electricity provider.The experimental results show that the use of feature extraction,sample generation,and semi-supervised learning model can improve the accuracy of transformer fault classification.This verifies the effectiveness of the proposed method.展开更多
The limited labeled sample data in the field of advanced security threats detection seriously restricts the effective development of research work.Learning the sample labels from the labeled and unlabeled data has rec...The limited labeled sample data in the field of advanced security threats detection seriously restricts the effective development of research work.Learning the sample labels from the labeled and unlabeled data has received a lot of research attention and various universal labeling methods have been proposed.However,the labeling task of malicious communication samples targeted at advanced threats has to face the two practical challenges:the difficulty of extracting effective features in advance and the complexity of the actual sample types.To address these problems,we proposed a sample labeling method for malicious communication based on semi-supervised deep neural network.This method supports continuous learning and optimization feature representation while labeling sample,and can handle uncertain samples that are outside the concerned sample types.According to the experimental results,our proposed deep neural network can automatically learn effective feature representation,and the validity of features is close to or even higher than that of features which extracted based on expert knowledge.Furthermore,our proposed method can achieve the labeling accuracy of 97.64%~98.50%,which is more accurate than the train-then-detect,kNN and LPA methodsin any labeled-sample proportion condition.The problem of insufficient labeled samples in many network attack detecting scenarios,and our proposed work can function as a reference for the sample labeling tasks in the similar real-world scenarios.展开更多
The accuracy of laser-induced breakdown spectroscopy(LIBS) quantitative method is greatly dependent on the amount of certified standard samples used for training. However, in practical applications, only limited stand...The accuracy of laser-induced breakdown spectroscopy(LIBS) quantitative method is greatly dependent on the amount of certified standard samples used for training. However, in practical applications, only limited standard samples with labeled certified concentrations are available. A novel semi-supervised LIBS quantitative analysis method is proposed, based on co-training regression model with selection of effective unlabeled samples. The main idea of the proposed method is to obtain better regression performance by adding effective unlabeled samples in semisupervised learning. First, effective unlabeled samples are selected according to the testing samples by Euclidean metric. Two original regression models based on least squares support vector machine with different parameters are trained by the labeled samples separately, and then the effective unlabeled samples predicted by the two models are used to enlarge the training dataset based on labeling confidence estimation. The final predictions of the proposed method on the testing samples will be determined by weighted combinations of the predictions of two updated regression models. Chromium concentration analysis experiments of 23 certified standard high-alloy steel samples were carried out, in which 5 samples with labeled concentrations and 11 unlabeled samples were used to train the regression models and the remaining 7 samples were used for testing. With the numbers of effective unlabeled samples increasing, the root mean square error of the proposed method went down from 1.80% to 0.84% and the relative prediction error was reduced from 9.15% to 4.04%.展开更多
To discover personalized document structure with the consideration of user preferences,user preferences were captured by limited amount of instance level constraints and given as interested and uninterested key terms....To discover personalized document structure with the consideration of user preferences,user preferences were captured by limited amount of instance level constraints and given as interested and uninterested key terms.Develop a semi-supervised document clustering approach based on the latent Dirichlet allocation(LDA)model,namely,pLDA,guided by the user provided key terms.Propose a generalized Polya urn(GPU) model to integrate the user preferences to the document clustering process.A Gibbs sampler was investigated to infer the document collection structure.Experiments on real datasets were taken to explore the performance of pLDA.The results demonstrate that the pLDA approach is effective.展开更多
Non-collaborative radio transmitter recognition is a significant but challenging issue, since it is hard or costly to obtain labeled training data samples. In order to make effective use of the unlabeled samples which...Non-collaborative radio transmitter recognition is a significant but challenging issue, since it is hard or costly to obtain labeled training data samples. In order to make effective use of the unlabeled samples which can be obtained much easier, a novel semi-supervised classification method named Elastic Sparsity Regularized Support Vector Machine (ESRSVM) is proposed for radio transmitter classification. ESRSVM first constructs an elastic-net graph over data samples to capture the robust and natural discriminating information and then incorporate the information into the manifold learning framework by an elastic sparsity regularization term. Experimental results on 10 GMSK modulated Automatic Identification System radios and 15 FM walkie-talkie radios show that ESRSVM achieves obviously better performance than KNN and SVM, which use only labeled samples for classification, and also outperforms semi-supervised classifier LapSVM based on manifold regularization.展开更多
For the classification problem in practice,one of the challenging issues is to obtain enough labeled data for training.Moreover,even if such labeled data has been sufficiently accumulated,most datasets often exhibit l...For the classification problem in practice,one of the challenging issues is to obtain enough labeled data for training.Moreover,even if such labeled data has been sufficiently accumulated,most datasets often exhibit long-tailed distribution with heavy class imbalance,which results in a biased model towards a majority class.To alleviate such class imbalance,semisupervised learning methods using additional unlabeled data have been considered.However,as a matter of course,the accuracy is much lower than that from supervised learning.In this study,under the assumption that additional unlabeled data is available,we propose the iterative semi-supervised learning algorithms,which iteratively correct the labeling of the extra unlabeled data based on softmax probabilities.The results show that the proposed algorithms provide the accuracy as high as that from the supervised learning.To validate the proposed algorithms,we tested on the two scenarios:with the balanced unlabeled dataset and with the imbalanced unlabeled dataset.Under both scenarios,our proposed semi-supervised learning algorithms provided higher accuracy than previous state-of-the-arts.Code is available at https://github.com/HeewonChung92/iterative-semi-learning.展开更多
文摘The aim of this paper is to broaden the application of Stochastic Configuration Network (SCN) in the semi-supervised domain by utilizing common unlabeled data in daily life. It can enhance the classification accuracy of decentralized SCN algorithms while effectively protecting user privacy. To this end, we propose a decentralized semi-supervised learning algorithm for SCN, called DMT-SCN, which introduces teacher and student models by combining the idea of consistency regularization to improve the response speed of model iterations. In order to reduce the possible negative impact of unsupervised data on the model, we purposely change the way of adding noise to the unlabeled data. Simulation results show that the algorithm can effectively utilize unlabeled data to improve the classification accuracy of SCN training and is robust under different ground simulation environments.
基金supported by Innovation Talents Promotion Program of Shaanxi Province,China(No.2021TD08)。
文摘Radio frequency fingerprinting(RFF)is a remarkable lightweight authentication scheme to support rapid and scalable identification in the internet of things(IoT)systems.Deep learning(DL)is a critical enabler of RFF identification by leveraging the hardware-level features.However,traditional supervised learning methods require huge labeled training samples.Therefore,how to establish a highperformance supervised learning model with few labels under practical application is still challenging.To address this issue,we in this paper propose a novel RFF semi-supervised learning(RFFSSL)model which can obtain a better performance with few meta labels.Specifically,the proposed RFFSSL model is constituted by a teacher-student network,in which the student network learns from the pseudo label predicted by the teacher.Then,the output of the student model will be exploited to improve the performance of teacher among the labeled data.Furthermore,a comprehensive evaluation on the accuracy is conducted.We derive about 50 GB real long-term evolution(LTE)mobile phone’s raw signal datasets,which is used to evaluate various models.Experimental results demonstrate that the proposed RFFSSL scheme can achieve up to 97%experimental testing accuracy over a noisy environment only with 10%labeled samples when training samples equal to 2700.
基金supported by Korea Institute for Advancement of Technology(KIAT)grant funded by theKoreaGovernment(MOTIE)(P0008703,The CompetencyDevelopment Program for Industry Specialist).
文摘Intrusion detection involves identifying unauthorized network activity and recognizing whether the data constitute an abnormal network transmission.Recent research has focused on using semi-supervised learning mechanisms to identify abnormal network traffic to deal with labeled and unlabeled data in the industry.However,real-time training and classifying network traffic pose challenges,as they can lead to the degradation of the overall dataset and difficulties preventing attacks.Additionally,existing semi-supervised learning research might need to analyze the experimental results comprehensively.This paper proposes XA-GANomaly,a novel technique for explainable adaptive semi-supervised learning using GANomaly,an image anomalous detection model that dynamically trains small subsets to these issues.First,this research introduces a deep neural network(DNN)-based GANomaly for semi-supervised learning.Second,this paper presents the proposed adaptive algorithm for the DNN-based GANomaly,which is validated with four subsets of the adaptive dataset.Finally,this study demonstrates a monitoring system that incorporates three explainable techniques—Shapley additive explanations,reconstruction error visualization,and t-distributed stochastic neighbor embedding—to respond effectively to attacks on traffic data at each feature engineering stage,semi-supervised learning,and adaptive learning.Compared to other single-class classification techniques,the proposed DNN-based GANomaly achieves higher scores for Network Security Laboratory-Knowledge Discovery in Databases and UNSW-NB15 datasets at 13%and 8%of F1 scores and 4.17%and 11.51%for accuracy,respectively.Furthermore,experiments of the proposed adaptive learning reveal mostly improved results over the initial values.An analysis and monitoring system based on the combination of the three explainable methodologies is also described.Thus,the proposed method has the potential advantages to be applied in practical industry,and future research will explore handling unbalanced real-time datasets in various scenarios.
基金The publication of this article is funded by the Qatar National Library.
文摘Malaria is a lethal disease responsible for thousands of deaths worldwide every year.Manual methods of malaria diagnosis are timeconsuming that require a great deal of human expertise and efforts.Computerbased automated diagnosis of diseases is progressively becoming popular.Although deep learning models show high performance in the medical field,it demands a large volume of data for training which is hard to acquire for medical problems.Similarly,labeling of medical images can be done with the help of medical experts only.Several recent studies have utilized deep learning models to develop efficient malaria diagnostic system,which showed promising results.However,the most common problem with these models is that they need a large amount of data for training.This paper presents a computer-aided malaria diagnosis system that combines a semi-supervised generative adversarial network and transfer learning.The proposed model is trained in a semi-supervised manner and requires less training data than conventional deep learning models.Performance of the proposed model is evaluated on a publicly available dataset of blood smear images(with malariainfected and normal class)and achieved a classification accuracy of 96.6%.
基金supported by the Fifth Key Project of Jiangsu Vocational Education Teaching Reform Research under Grant ZZZ13in part by the Science and Technology Project of Changzhou City under Grant CE20215032.
文摘Through semi-supervised learning and knowledge inheritance,a novel Takagi-Sugeno-Kang(TSK)fuzzy system framework is proposed for epilepsy data classification in this study.The new method is based on the maximum mean discrepancy(MMD)method and TSK fuzzy system,as a basic model for the classification of epilepsy data.First,formedical data,the interpretability of TSK fuzzy systems can ensure that the prediction results are traceable and safe.Second,in view of the deviation in the data distribution between the real source domain and the target domain,MMD is used to measure the distance between different data distributions.The objective function is constructed according to the MMD distance,and the distribution distance of different datasets is minimized to find the similar characteristics of different datasets.We introduce semi-supervised learning to further explore the relationship between data.Based on the MMD method,a semi-supervised learning(SSL)-MMD method is constructed by using pseudo-tags to realize the data distribution alignment of the same category.In addition,the idea of knowledge dissemination is used to learn pseudo-tags as additional data features.Finally,for epilepsy classification,the cross-domain TSK fuzzy system uses the cross-entropy function as the objective function and adopts the back-propagation strategy to optimize the parameters.The experimental results show that the new method can process complex epilepsy data and identify whether patients have epilepsy.
基金supported in part by the National Key R&D Program of China under Grant 2018YFA0701601part by the National Natural Science Foundation of China(Grant No.U22A2002,61941104,62201605)part by Tsinghua University-China Mobile Communications Group Co.,Ltd.Joint Institute。
文摘In the upcoming large-scale Internet of Things(Io T),it is increasingly challenging to defend against malicious traffic,due to the heterogeneity of Io T devices and the diversity of Io T communication protocols.In this paper,we propose a semi-supervised learning-based approach to detect malicious traffic at the access side.It overcomes the resource-bottleneck problem of traditional malicious traffic defenders which are deployed at the victim side,and also is free of labeled traffic data in model training.Specifically,we design a coarse-grained behavior model of Io T devices by self-supervised learning with unlabeled traffic data.Then,we fine-tune this model to improve its accuracy in malicious traffic detection by adopting a transfer learning method using a small amount of labeled data.Experimental results show that our method can achieve the accuracy of 99.52%and the F1-score of 99.52%with only 1%of the labeled training data based on the CICDDoS2019 dataset.Moreover,our method outperforms the stateof-the-art supervised learning-based methods in terms of accuracy,precision,recall and F1-score with 1%of the training data.
基金This research is supported by Fundamental Research Grant Scheme(FRGS),Ministry of Education Malaysia(MOE)under the project code,FRGS/1/2018/ICT02/USM/02/9 titled,Automated Big Data Annotation for Training Semi-Supervised Deep Learning Model in Sentiment Classification.
文摘Sentiment classification is a useful tool to classify reviews about sentiments and attitudes towards a product or service.Existing studies heavily rely on sentiment classification methods that require fully annotated inputs.However,there is limited labelled text available,making the acquirement process of the fully annotated input costly and labour-intensive.Lately,semi-supervised methods emerge as they require only partially labelled input but perform comparably to supervised methods.Nevertheless,some works reported that the performance of the semi-supervised model degraded after adding unlabelled instances into training.Literature also shows that not all unlabelled instances are equally useful;thus identifying the informative unlabelled instances is beneficial in training a semi-supervised model.To achieve this,an informative score is proposed and incorporated into semisupervised sentiment classification.The evaluation is performed on a semisupervised method without an informative score and with an informative score.By using the informative score in the instance selection strategy to identify informative unlabelled instances,semi-supervised models perform better compared to models that do not incorporate informative scores into their training.Although the performance of semi-supervised models incorporated with an informative score is not able to surpass the supervised models,the results are still found promising as the differences in performance are subtle with a small difference of 2%to 5%,but the number of labelled instances used is greatly reduced from100%to 40%.The best finding of the proposed instance selection strategy is achieved when incorporating an informative score with a baseline confidence score at a 0.5:0.5 ratio using only 40%labelled data.
基金Project supported by the Open Fund of Advanced Cryptography and System Security Key Laboratory of Sichuan Province(Grant No.SKLACSS-202108)the National Natural Science Foundation of China(Grant No.U162271070)Scientific Research Fund of Zaozhuang University(Grant No.102061901).
文摘Label propagation is an essential semi-supervised learning method based on graphs,which has a broad spectrum of applications in pattern recognition and data mining.This paper proposes a quantum semi-supervised classifier based on label propagation.Considering the difficulty of graph construction,we develop a variational quantum label propagation(VQLP)method.In this method,a locally parameterized quantum circuit is created to reduce the parameters required in the optimization.Furthermore,we design a quantum semi-supervised binary classifier based on hybrid Bell and Z bases measurement,which has a shallower circuit depth and is more suitable for implementation on near-term quantum devices.We demonstrate the performance of the quantum semi-supervised classifier on the Iris data set,and the simulation results show that the quantum semi-supervised classifier has higher classification accuracy than the swap test classifier.This work opens a new path to quantum machine learning based on graphs.
基金supported by the National Natural Science Foundation of China (Nos.62072127,62002076,61906049)Natural Science Foundation of Guangdong Province (Nos.2023A1515011774,2020A1515010423)+4 种基金Project 6142111180404 supported by CNKLSTISS,Science and Technology Program of Guangzhou,China (No.202002030131)Guangdong basic and applied basic research fund joint fund Youth Fund (No.2019A1515110213)Open Fund Project of Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University) (No.MJUKF-IPIC202101)Natural Science Foundation of Guangdong Province No.2020A1515010423)Scientific research project for Guangzhou University (No.RP2022003).
文摘Recent state-of-the-art semi-supervised learning(SSL)methods usually use data augmentations as core components.Such methods,however,are limited to simple transformations such as the augmentations under the instance’s naive representations or the augmentations under the instance’s semantic representations.To tackle this problem,we offer a unique insight into data augmentations and propose a novel data-augmentation-based semi-supervised learning method,called Attentive Neighborhood Feature Aug-mentation(ANFA).The motivation of our method lies in the observation that the relationship between the given feature and its neighborhood may contribute to constructing more reliable transformations for the data,and further facilitating the classifier to distinguish the ambiguous features from the low-dense regions.Specially,we first project the labeled and unlabeled data points into an embedding space and then construct a neighbor graph that serves as a similarity measure based on the similar representations in the embedding space.Then,we employ an attention mechanism to transform the target features into augmented ones based on the neighbor graph.Finally,we formulate a novel semi-supervised loss by encouraging the predictions of the interpolations of augmented features to be consistent with the corresponding interpolations of the predictions of the target features.We carried out exper-iments on SVHN and CIFAR-10 benchmark datasets and the experimental results demonstrate that our method outperforms the state-of-the-art methods when the number of labeled examples is limited.
基金This research is funded by Graduate University of Science and Technology under grant number GUST.STS.DT2020-TT01。
文摘Clustering is a crucial method for deciphering data structure and producing new information.Due to its significance in revealing fundamental connections between the human brain and events,it is essential to utilize clustering for cognitive research.Dealing with noisy data caused by inaccurate synthesis from several sources or misleading data production processes is one of the most intriguing clustering difficulties.Noisy data can lead to incorrect object recognition and inference.This research aims to innovate a novel clustering approach,named Picture-Neutrosophic Trusted Safe Semi-Supervised Fuzzy Clustering(PNTS3FCM),to solve the clustering problem with noisy data using neutral and refusal degrees in the definition of Picture Fuzzy Set(PFS)and Neutrosophic Set(NS).Our contribution is to propose a new optimization model with four essential components:clustering,outlier removal,safe semi-supervised fuzzy clustering and partitioning with labeled and unlabeled data.The effectiveness and flexibility of the proposed technique are estimated and compared with the state-of-art methods,standard Picture fuzzy clustering(FC-PFS)and Confidence-weighted safe semi-supervised clustering(CS3FCM)on benchmark UCI datasets.The experimental results show that our method is better at least 10/15 datasets than the compared methods in terms of clustering quality and computational time.
文摘Clustering analysis is one of the main concerns in data mining.A common approach to the clustering process is to bring together points that are close to each other and separate points that are away from each other.Therefore,measuring the distance between sample points is crucial to the effectiveness of clustering.Filtering features by label information and mea-suring the distance between samples by these features is a common supervised learning method to reconstruct distance metric.However,in many application scenarios,it is very expensive to obtain a large number of labeled samples.In this paper,to solve the clustering problem in the few supervised sample and high data dimensionality scenarios,a novel semi-supervised clustering algorithm is proposed by designing an improved prototype network that attempts to reconstruct the distance metric in the sample space with a small amount of pairwise supervised information,such as Must-Link and Cannot-Link,and then cluster the data in the new metric space.The core idea is to make the similar ones closer and the dissimilar ones further away through embedding mapping.Extensive experiments on both real-world and synthetic datasets show the effectiveness of this algorithm.Average clustering metrics on various datasets improved by 8%compared to the comparison algorithm.
基金This work is supported by the National Natural Science Foundation of China(Nos.61771154,61603239,61772454,6171101570).
文摘Deep Learning(DL)is such a powerful tool that we have seen tremendous success in areas such as Computer Vision,Speech Recognition,and Natural Language Processing.Since Automated Modulation Classification(AMC)is an important part in Cognitive Radio Networks,we try to explore its potential in solving signal modulation recognition problem.It cannot be overlooked that DL model is a complex model,thus making them prone to over-fitting.DL model requires many training data to combat with over-fitting,but adding high quality labels to training data manually is not always cheap and accessible,especially in real-time system,which may counter unprecedented data in dataset.Semi-supervised Learning is a way to exploit unlabeled data effectively to reduce over-fitting in DL.In this paper,we extend Generative Adversarial Networks(GANs)to the semi-supervised learning will show it is a method can be used to create a more dataefficient classifier.
文摘A Machine Learning (ML)-based Intrusion Detection and Prevention System (IDPS)requires a large amount of labeled up-to-date training data to effectively detect intrusions and generalize well to novel attacks.However,the labeling of data is costly and becomes infeasible when dealing with big data,such as those generated by Intemet of Things applications.To this effect,building an ML model that learns from non-labeled or partially labeled data is of critical importance.This paper proposes a Semi-supervised Mniti-Layered Clustering ((SMLC))model for the detection and prevention of network intrusion.SMLC has the capability to learn from partially labeled data while achieving a detection performance comparable to that of supervised ML-based IDPS.The performance of SMLC is compared with that of a well-known semi-supervised model (tri-training)and of supervised ensemble ML models, namely Random.Forest,Bagging,and AdaboostM1on two benchmark network-intrusion datasets,NSL and Kyoto 2006+.Experimental resnits show that SMLC is superior to tri-training,providing a comparable detection accuracy with 20%less labeled instances of training data.Furthermore,our results demonstrate that our scheme has a detection accuracy comparable to that of the supervised ensemble models.
文摘The majority of big data analytics applied to transportation datasets suffer from being too domain-specific,that is,they draw conclusions for a dataset based on analytics on the same dataset.This makes models trained from one domain(e.g.taxi data)applies badly to a different domain(e.g.Uber data).To achieve accurate analyses on a new domain,substantial amounts of data must be available,which limits practical applications.To remedy this,we propose to use semi-supervised and active learning of big data to accomplish the domain adaptation task:Selectively choosing a small amount of datapoints from a new domain while achieving comparable performances to using all the datapoints.We choose the New York City(NYC)transportation data of taxi and Uber as our dataset,simulating different domains with 90%as the source data domain for training and the remaining 10%as the target data domain for evaluation.We propose semi-supervised and active learning strategies and apply it to the source domain for selecting datapoints.Experimental results show that our adaptation achieves a comparable performance of using all datapoints while using only a fraction of them,substantially reducing the amount of data required.Our approach has two major advantages:It can make accurate analytics and predictions when big datasets are not available,and even if big datasets are available,our approach chooses the most informative datapoints out of the dataset,making the process much more efficient without having to process huge amounts of data.
基金supported by China Southern Power Grid Co.Ltd.science and technology project(Research on the theory,technology and application of stereoscopic disaster defense for power distribution network in large city,GZHKJXM20180060)National Natural Science Foundation of China(No.51477100).
文摘It is crucial to maintain the safe and stable operation of distribution transformers,which constitute a key part of power systems.In the event of transformer failure,the fault type must be diagnosed in a timely and accurate manner.To this end,a transformer fault diagnosis method based on infrared image processing and semi-supervised learning is proposed herein.First,we perform feature extraction on the collected infrared-image data to extract temperature,texture,and shape features as the model reference vectors.Then,a generative adversarial network(GAN)is constructed to generate synthetic samples for the minority subset of labelled samples.The proposed method can learn information from unlabeled sample data,unlike conventional supervised learning methods.Subsequently,a semi-supervised graph model is trained on the entire dataset,i.e.,both labeled and unlabeled data.Finally,we test the proposed model on an actual dataset collected from a Chinese electricity provider.The experimental results show that the use of feature extraction,sample generation,and semi-supervised learning model can improve the accuracy of transformer fault classification.This verifies the effectiveness of the proposed method.
基金partially funded by the National Natural Science Foundation of China (Grant No. 61272447)National Entrepreneurship & Innovation Demonstration Base of China (Grant No. C700011)Key Research & Development Project of Sichuan Province of China (Grant No. 2018G20100)
文摘The limited labeled sample data in the field of advanced security threats detection seriously restricts the effective development of research work.Learning the sample labels from the labeled and unlabeled data has received a lot of research attention and various universal labeling methods have been proposed.However,the labeling task of malicious communication samples targeted at advanced threats has to face the two practical challenges:the difficulty of extracting effective features in advance and the complexity of the actual sample types.To address these problems,we proposed a sample labeling method for malicious communication based on semi-supervised deep neural network.This method supports continuous learning and optimization feature representation while labeling sample,and can handle uncertain samples that are outside the concerned sample types.According to the experimental results,our proposed deep neural network can automatically learn effective feature representation,and the validity of features is close to or even higher than that of features which extracted based on expert knowledge.Furthermore,our proposed method can achieve the labeling accuracy of 97.64%~98.50%,which is more accurate than the train-then-detect,kNN and LPA methodsin any labeled-sample proportion condition.The problem of insufficient labeled samples in many network attack detecting scenarios,and our proposed work can function as a reference for the sample labeling tasks in the similar real-world scenarios.
基金supported by National Natural Science Foundation of China (No. 51674032)
文摘The accuracy of laser-induced breakdown spectroscopy(LIBS) quantitative method is greatly dependent on the amount of certified standard samples used for training. However, in practical applications, only limited standard samples with labeled certified concentrations are available. A novel semi-supervised LIBS quantitative analysis method is proposed, based on co-training regression model with selection of effective unlabeled samples. The main idea of the proposed method is to obtain better regression performance by adding effective unlabeled samples in semisupervised learning. First, effective unlabeled samples are selected according to the testing samples by Euclidean metric. Two original regression models based on least squares support vector machine with different parameters are trained by the labeled samples separately, and then the effective unlabeled samples predicted by the two models are used to enlarge the training dataset based on labeling confidence estimation. The final predictions of the proposed method on the testing samples will be determined by weighted combinations of the predictions of two updated regression models. Chromium concentration analysis experiments of 23 certified standard high-alloy steel samples were carried out, in which 5 samples with labeled concentrations and 11 unlabeled samples were used to train the regression models and the remaining 7 samples were used for testing. With the numbers of effective unlabeled samples increasing, the root mean square error of the proposed method went down from 1.80% to 0.84% and the relative prediction error was reduced from 9.15% to 4.04%.
基金National Natural Science Foundations of China(Nos.61262006,61462011,61202089)the Major Applied Basic Research Program of Guizhou Province Project,China(No.JZ20142001)+2 种基金the Science and Technology Foundation of Guizhou Province Project,China(No.LH20147636)the National Research Foundation for the Doctoral Program of Higher Education of China(No.20125201120006)the Graduate Innovated Foundations of Guizhou University Project,China(No.2015012)
文摘To discover personalized document structure with the consideration of user preferences,user preferences were captured by limited amount of instance level constraints and given as interested and uninterested key terms.Develop a semi-supervised document clustering approach based on the latent Dirichlet allocation(LDA)model,namely,pLDA,guided by the user provided key terms.Propose a generalized Polya urn(GPU) model to integrate the user preferences to the document clustering process.A Gibbs sampler was investigated to infer the document collection structure.Experiments on real datasets were taken to explore the performance of pLDA.The results demonstrate that the pLDA approach is effective.
基金Supported by the Hi-Tech Research and Development Program of China (No. 2009AAJ130)
文摘Non-collaborative radio transmitter recognition is a significant but challenging issue, since it is hard or costly to obtain labeled training data samples. In order to make effective use of the unlabeled samples which can be obtained much easier, a novel semi-supervised classification method named Elastic Sparsity Regularized Support Vector Machine (ESRSVM) is proposed for radio transmitter classification. ESRSVM first constructs an elastic-net graph over data samples to capture the robust and natural discriminating information and then incorporate the information into the manifold learning framework by an elastic sparsity regularization term. Experimental results on 10 GMSK modulated Automatic Identification System radios and 15 FM walkie-talkie radios show that ESRSVM achieves obviously better performance than KNN and SVM, which use only labeled samples for classification, and also outperforms semi-supervised classifier LapSVM based on manifold regularization.
基金This work was supported by the National Research Foundation of Korea(No.2020R1A2C1014829)by the Korea Medical Device Development Fund grant,which is funded by the Government of the Republic of Korea Korea government(the Ministry of Science and ICT+2 种基金the Ministry of Trade,Industry and Energythe Ministry of Health and Welfareand the Ministry of Food and Drug Safety)(grant KMDF_PR_20200901_0095).
文摘For the classification problem in practice,one of the challenging issues is to obtain enough labeled data for training.Moreover,even if such labeled data has been sufficiently accumulated,most datasets often exhibit long-tailed distribution with heavy class imbalance,which results in a biased model towards a majority class.To alleviate such class imbalance,semisupervised learning methods using additional unlabeled data have been considered.However,as a matter of course,the accuracy is much lower than that from supervised learning.In this study,under the assumption that additional unlabeled data is available,we propose the iterative semi-supervised learning algorithms,which iteratively correct the labeling of the extra unlabeled data based on softmax probabilities.The results show that the proposed algorithms provide the accuracy as high as that from the supervised learning.To validate the proposed algorithms,we tested on the two scenarios:with the balanced unlabeled dataset and with the imbalanced unlabeled dataset.Under both scenarios,our proposed semi-supervised learning algorithms provided higher accuracy than previous state-of-the-arts.Code is available at https://github.com/HeewonChung92/iterative-semi-learning.
