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Learning Proximity Relations for Feature Selection
Taiping Zhang1; Pengfei Ren2; Yao Ge2; Yali Zheng3; Yuan Yan Tang4; C.L. Philip Chen4
2016-05-01
Source PublicationIEEE Transactions on Knowledge and Data Engineering
ISSN1041-4347
Volume28Issue:5Pages:1231-1244
Abstract

This work presents a feature selection method based on proximity relations learning. Each single feature is treated as a binary classifier that predicts for any three objects X, A, and B whether X is close to A or B. The performance of the classifier is a direct measure of feature quality. Any linear combination of feature-based binary classifiers naturally corresponds to feature selection. Thus, the feature selection problem is transformed into an ensemble learning problem of combining many weak classifiers into an optimized strong classifier. We provide a theoretical analysis of the generalization error of our proposed method which validates the effectiveness of our proposed method. Various experiments are conducted on synthetic data, four UCI data sets and 12 microarray data sets, and demonstrate the success of our approach applying to feature selection. A weakness of our algorithm is high time complexity.

KeywordClassification Feature Evaluation Feature Selection Gene Selection Microarray Analysis
DOIhttps://doi.org/10.1109/TKDE.2016.2515588
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic
WOS IDWOS:000374523000011
PublisherIEEE COMPUTER SOC, 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
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Citation statistics
Cited Times [WOS]:1   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorTaiping Zhang; Pengfei Ren; Yao Ge; Yali Zheng; Yuan Yan Tang; C.L. Philip Chen
Affiliation1.College of Computer Science, Institute of Computing and Data Sciences, Chongqing University, Chongqing 400030, P.R. China
2.College of Computer Science, Chongqing University, Chongqing 400030, P.R. China.
3.School of Automation Engineering, University of Electronic Science and Technology of China, 2006 Xiyuan Avenue, Chengdu, China.
4.Faculty of Science and Technology, University of Macau, Avenida da Universidade, Taipa, Macau, China
Corresponding Author AffilicationFaculty of Science and Technology
Recommended Citation
GB/T 7714
Taiping Zhang,Pengfei Ren,Yao Ge,et al. Learning Proximity Relations for Feature Selection[J]. IEEE Transactions on Knowledge and Data Engineering,2016,28(5):1231-1244.
APA Taiping Zhang,Pengfei Ren,Yao Ge,Yali Zheng,Yuan Yan Tang,&C.L. Philip Chen.(2016).Learning Proximity Relations for Feature Selection.IEEE Transactions on Knowledge and Data Engineering,28(5),1231-1244.
MLA Taiping Zhang,et al."Learning Proximity Relations for Feature Selection".IEEE Transactions on Knowledge and Data Engineering 28.5(2016):1231-1244.
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