Current studentsExisting staffContact us
 

Supervised feature selection algorithm via discriminative ridge regression

Simple item page
dc.contributor.authorZhang, S.
dc.contributor.authorCheng, D.
dc.contributor.authorHu, R.
dc.contributor.authorDeng, Z.
dc.date.issued2017
dc.description.abstractThis paper studies a new feature selection method for data classification that efficiently combines the discriminative capability of features with the ridge regression model. It first sets up the global structure of training data with the linear discriminant analysis that assists in identifying the discriminative features. And then, the ridge regression model is employed to assess the feature representation and the discrimination information, so as to obtain the representative coefficient matrix. The importance of features can be calculated with this representative coefficient matrix. Finally, the new subset of selected features is applied to a linear Support Vector Machine for data classification. To validate the efficiency, sets of experiments are conducted with twenty benchmark datasets. The experimental results show that the proposed approach performs much better than the state-of-the-art feature selection algorithms in terms of the evaluating indicator of classification. And the proposed feature selection algorithm possesses a competitive performance compared with existing feature selection algorithms with regard to the computational cost.
dc.identifier.citationWorld Wide Web, 2017; 21(6):1545-1562
dc.identifier.doi10.1007/s11280-017-0502-9
dc.identifier.issn1386-145X
dc.identifier.issn1573-1413
dc.identifier.urihttps://hdl.handle.net/11541.2/128922
dc.language.isoen
dc.publisherSpringer New York
dc.rightsCopyright 2017 Springer Science+Business Media Access Condition Notes: Accepted manuscritp available after 1 January 2019
dc.source.urihttps://doi.org/10.1007/s11280-017-0502-9
dc.subjectridge regression
dc.subjectlinear discriminant analysis
dc.subjectrepresentative coefficient matrix
dc.subjectsupport vector machine
dc.titleSupervised feature selection algorithm via discriminative ridge regression
dc.typeJournal article
pubs.publication-statusPublished
ror.fileinfo12150088860001831 13150088850001831 9916158394001831_Postprint
ror.mmsid9916158394001831

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
9916158394001831_12150088860001831_WWW_DRRFS.pdf
Size:
374.23 KB
Format:
Adobe Portable Document Format
Description:
Published version
Download

Collections