Every application in a smart city environment like the smart grid,health monitoring, security, and surveillance generates non-stationary datastreams. Due to such nature, the statistical properties of data changes over...Every application in a smart city environment like the smart grid,health monitoring, security, and surveillance generates non-stationary datastreams. Due to such nature, the statistical properties of data changes overtime, leading to class imbalance and concept drift issues. Both these issuescause model performance degradation. Most of the current work has beenfocused on developing an ensemble strategy by training a new classifier on thelatest data to resolve the issue. These techniques suffer while training the newclassifier if the data is imbalanced. Also, the class imbalance ratio may changegreatly from one input stream to another, making the problem more complex.The existing solutions proposed for addressing the combined issue of classimbalance and concept drift are lacking in understating of correlation of oneproblem with the other. This work studies the association between conceptdrift and class imbalance ratio and then demonstrates how changes in classimbalance ratio along with concept drift affect the classifier’s performance.We analyzed the effect of both the issues on minority and majority classesindividually. To do this, we conducted experiments on benchmark datasetsusing state-of-the-art classifiers especially designed for data stream classification.Precision, recall, F1 score, and geometric mean were used to measure theperformance. Our findings show that when both class imbalance and conceptdrift problems occur together the performance can decrease up to 15%. Ourresults also show that the increase in the imbalance ratio can cause a 10% to15% decrease in the precision scores of both minority and majority classes.The study findings may help in designing intelligent and adaptive solutionsthat can cope with the challenges of non-stationary data streams like conceptdrift and class imbalance.展开更多
Fuzzy inference system(FIS)is a process of fuzzy logic reasoning to produce the output based on fuzzified inputs.The system starts with identifying input from data,applying the fuzziness to input using membership func...Fuzzy inference system(FIS)is a process of fuzzy logic reasoning to produce the output based on fuzzified inputs.The system starts with identifying input from data,applying the fuzziness to input using membership functions(MF),generating fuzzy rules for the fuzzy sets and obtaining the output.There are several types of input MFs which can be introduced in FIS,commonly chosen based on the type of real data,sensitivity of certain rule implied and computational limits.This paper focuses on the construction of interval type 2(IT2)trapezoidal shape MF from fuzzy C Means(FCM)that is used for fuzzification process of mamdani FIS.In the process,upper MF(UMF)and lower MF(LMF)of the MF need to be identified to get the range of the footprint of uncertainty(FOU).This paper proposes Genetic tuning process,which is a part of genetic algorithm(GA),to adjust parameters in order to improve the behavior of existing system,especially to enhance the accuracy of the system model.This novel process is a hybrid approach which produces Genetic Fuzzy System(GFS)that helps to enhance fuzzy classification problems and performance.The approach provides a new method for the construction and tuning process of the IT2 MF,based on the FCM outcomes.The result is compared to Gaussian shape IT2 MF and trapezoid IT2 MF generated by the classic GA method.It is shown that the proposed approach is able to outperform the mentioned benchmarked approaches.The work implies a wider range of IT2 MF types,constructed based on FCM outcomes,and an optimum generation of the FOU so that it can be implemented in practical applications such as prediction,analytics and rule-based solutions.展开更多
Student performance prediction helps the educational stakeholders to take proactive decisions and make interventions,for the improvement of quality of education and to meet the dynamic needs of society.The selection o...Student performance prediction helps the educational stakeholders to take proactive decisions and make interventions,for the improvement of quality of education and to meet the dynamic needs of society.The selection of features for student’s performance prediction not only plays significant role in increasing prediction accuracy,but also helps in building the strategic plans for the improvement of students’academic performance.There are different feature selection algorithms for predicting the performance of students,however the studies reported in the literature claim that there are different pros and cons of existing feature selection algorithms in selection of optimal features.In this paper,a hybrid feature selection framework(using feature-fusion)is designed to identify the significant features and associated features with target class,to predict the performance of students.The main goal of the proposed hybrid feature selection is not only to improve the prediction accuracy,but also to identify optimal features for building productive strategies for the improvement in students’academic performance.The key difference between proposed hybrid feature selection framework and existing hybrid feature selection framework,is two level feature fusion technique,with the utilization of cosine-based fusion.Whereas,according to the results reported in existing literature,cosine similarity is considered as the best similarity measure among existing similarity measures.The proposed hybrid feature selection is validated on four benchmark datasets with variations in number of features and number of instances.The validated results confirm that the proposed hybrid feature selection framework performs better than the existing hybrid feature selection framework,existing feature selection algorithms in terms of accuracy,f-measure,recall,and precision.Results reported in presented paper show that the proposed approach gives more than 90%accuracy on benchmark dataset that is better than the results of existing approach.展开更多
基金The authors would like to extend their gratitude to Universiti Teknologi PETRONAS (Malaysia)for funding this research through grant number (015LA0-037).
文摘Every application in a smart city environment like the smart grid,health monitoring, security, and surveillance generates non-stationary datastreams. Due to such nature, the statistical properties of data changes overtime, leading to class imbalance and concept drift issues. Both these issuescause model performance degradation. Most of the current work has beenfocused on developing an ensemble strategy by training a new classifier on thelatest data to resolve the issue. These techniques suffer while training the newclassifier if the data is imbalanced. Also, the class imbalance ratio may changegreatly from one input stream to another, making the problem more complex.The existing solutions proposed for addressing the combined issue of classimbalance and concept drift are lacking in understating of correlation of oneproblem with the other. This work studies the association between conceptdrift and class imbalance ratio and then demonstrates how changes in classimbalance ratio along with concept drift affect the classifier’s performance.We analyzed the effect of both the issues on minority and majority classesindividually. To do this, we conducted experiments on benchmark datasetsusing state-of-the-art classifiers especially designed for data stream classification.Precision, recall, F1 score, and geometric mean were used to measure theperformance. Our findings show that when both class imbalance and conceptdrift problems occur together the performance can decrease up to 15%. Ourresults also show that the increase in the imbalance ratio can cause a 10% to15% decrease in the precision scores of both minority and majority classes.The study findings may help in designing intelligent and adaptive solutionsthat can cope with the challenges of non-stationary data streams like conceptdrift and class imbalance.
基金The works presented in this paper are part of an ongoing research funded by the Fundamental Research Grant Scheme(FRGS/1/2018/ICT02/UTP/02/1)a grant funded by the Ministry of Higher Education,Malaysia and the Yayasan Universiti Teknologi PETRONAS grant(015LC0-274 and 015LC0-311).
文摘Fuzzy inference system(FIS)is a process of fuzzy logic reasoning to produce the output based on fuzzified inputs.The system starts with identifying input from data,applying the fuzziness to input using membership functions(MF),generating fuzzy rules for the fuzzy sets and obtaining the output.There are several types of input MFs which can be introduced in FIS,commonly chosen based on the type of real data,sensitivity of certain rule implied and computational limits.This paper focuses on the construction of interval type 2(IT2)trapezoidal shape MF from fuzzy C Means(FCM)that is used for fuzzification process of mamdani FIS.In the process,upper MF(UMF)and lower MF(LMF)of the MF need to be identified to get the range of the footprint of uncertainty(FOU).This paper proposes Genetic tuning process,which is a part of genetic algorithm(GA),to adjust parameters in order to improve the behavior of existing system,especially to enhance the accuracy of the system model.This novel process is a hybrid approach which produces Genetic Fuzzy System(GFS)that helps to enhance fuzzy classification problems and performance.The approach provides a new method for the construction and tuning process of the IT2 MF,based on the FCM outcomes.The result is compared to Gaussian shape IT2 MF and trapezoid IT2 MF generated by the classic GA method.It is shown that the proposed approach is able to outperform the mentioned benchmarked approaches.The work implies a wider range of IT2 MF types,constructed based on FCM outcomes,and an optimum generation of the FOU so that it can be implemented in practical applications such as prediction,analytics and rule-based solutions.
文摘Student performance prediction helps the educational stakeholders to take proactive decisions and make interventions,for the improvement of quality of education and to meet the dynamic needs of society.The selection of features for student’s performance prediction not only plays significant role in increasing prediction accuracy,but also helps in building the strategic plans for the improvement of students’academic performance.There are different feature selection algorithms for predicting the performance of students,however the studies reported in the literature claim that there are different pros and cons of existing feature selection algorithms in selection of optimal features.In this paper,a hybrid feature selection framework(using feature-fusion)is designed to identify the significant features and associated features with target class,to predict the performance of students.The main goal of the proposed hybrid feature selection is not only to improve the prediction accuracy,but also to identify optimal features for building productive strategies for the improvement in students’academic performance.The key difference between proposed hybrid feature selection framework and existing hybrid feature selection framework,is two level feature fusion technique,with the utilization of cosine-based fusion.Whereas,according to the results reported in existing literature,cosine similarity is considered as the best similarity measure among existing similarity measures.The proposed hybrid feature selection is validated on four benchmark datasets with variations in number of features and number of instances.The validated results confirm that the proposed hybrid feature selection framework performs better than the existing hybrid feature selection framework,existing feature selection algorithms in terms of accuracy,f-measure,recall,and precision.Results reported in presented paper show that the proposed approach gives more than 90%accuracy on benchmark dataset that is better than the results of existing approach.
