The Extreme Learning Machine（ELM） and its variants are effective in many machine learning applications such as Imbalanced Learning（IL） or Big Data（BD） learning. However, they are unable to solve both imbalanced ...The Extreme Learning Machine（ELM） and its variants are effective in many machine learning applications such as Imbalanced Learning（IL） or Big Data（BD） learning. However, they are unable to solve both imbalanced and large-volume data learning problems. This study addresses the IL problem in BD applications. The Distributed and Weighted ELM（DW-ELM） algorithm is proposed, which is based on the Map Reduce framework. To confirm the feasibility of parallel computation, first, the fact that matrix multiplication operators are decomposable is illustrated.Then, to further improve the computational efficiency, an Improved DW-ELM algorithm（IDW-ELM） is developed using only one Map Reduce job. The successful operations of the proposed DW-ELM and IDW-ELM algorithms are finally validated through experiments.展开更多
Detecting the boundaries of protein domains is an important and challenging task in both experimental and computational structural biology. In this paper, a promising method for detecting the domain structure of a pro...Detecting the boundaries of protein domains is an important and challenging task in both experimental and computational structural biology. In this paper, a promising method for detecting the domain structure of a protein from sequence information alone is presented. The method is based on analyzing multiple sequence alignments derived from a database search. Multiple measures are defined to quantify the domain information content of each position along the sequence. Then they are combined into a single predictor using support vector machine. What is more important, the domain detection is first taken as an imbal- anced data learning problem. A novel undersampling method is proposed on distance-based maximal entropy in the feature space of Support Vector Machine （SVM）. The overall precision is about 80%. Simulation results demonstrate that the method can help not only in predicting the complete 3D structure of a protein but also in the machine learning system on general im- balanced datasets.展开更多
基金partially supported by the National Natural Science Foundation of China(Nos.61402089,61472069,and 61501101)the Fundamental Research Funds for the Central Universities(Nos.N161904001,N161602003,and N150408001)+2 种基金the Natural Science Foundation of Liaoning Province(No.2015020553)the China Postdoctoral Science Foundation(No.2016M591447)the Postdoctoral Science Foundation of Northeastern University(No.20160203)
文摘The Extreme Learning Machine（ELM） and its variants are effective in many machine learning applications such as Imbalanced Learning（IL） or Big Data（BD） learning. However, they are unable to solve both imbalanced and large-volume data learning problems. This study addresses the IL problem in BD applications. The Distributed and Weighted ELM（DW-ELM） algorithm is proposed, which is based on the Map Reduce framework. To confirm the feasibility of parallel computation, first, the fact that matrix multiplication operators are decomposable is illustrated.Then, to further improve the computational efficiency, an Improved DW-ELM algorithm（IDW-ELM） is developed using only one Map Reduce job. The successful operations of the proposed DW-ELM and IDW-ELM algorithms are finally validated through experiments.
基金National Natural Science Foundation of China (Grant No. 60433020, 60673099, 60673023)"985" project of Jilin University
文摘Detecting the boundaries of protein domains is an important and challenging task in both experimental and computational structural biology. In this paper, a promising method for detecting the domain structure of a protein from sequence information alone is presented. The method is based on analyzing multiple sequence alignments derived from a database search. Multiple measures are defined to quantify the domain information content of each position along the sequence. Then they are combined into a single predictor using support vector machine. What is more important, the domain detection is first taken as an imbal- anced data learning problem. A novel undersampling method is proposed on distance-based maximal entropy in the feature space of Support Vector Machine （SVM）. The overall precision is about 80%. Simulation results demonstrate that the method can help not only in predicting the complete 3D structure of a protein but also in the machine learning system on general im- balanced datasets.
