Feature selection based on sparse imputation

Jin Xu, Yafeng Yin, Hong Man, Haibo He

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

26 Scopus citations

Abstract

Feature selection, which aims to obtain valuable feature subsets, has been an active topic for years. How to design an evaluating metric is the key for feature selection. In this paper, we address this problem using imputation quality to search for the meaningful features and propose feature selection via sparse imputation (FSSI) method. The key idea is utilizing sparse representation criterion to test individual feature. The feature based classification is used to evaluate the proposed method. Comparative studies are conducted with classic feature selection methods (such as Fisher score and Laplacian score). Experimental results on benchmark data sets demonstrate the effectiveness of FSSI method.

Original languageEnglish
Title of host publication2012 International Joint Conference on Neural Networks, IJCNN 2012
DOIs
StatePublished - 2012
Event2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Part of the 2012 IEEE World Congress on Computational Intelligence, WCCI 2012 - Brisbane, QLD, Australia
Duration: 10 Jun 201215 Jun 2012

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Conference

Conference2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Part of the 2012 IEEE World Congress on Computational Intelligence, WCCI 2012
Country/TerritoryAustralia
CityBrisbane, QLD
Period10/06/1215/06/12

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