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Class information adapted kernel for support vector machine

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conference contribution
posted on 06.12.2017, 00:00 authored by Tasadduq ImamTasadduq Imam, Kevin TickleKevin Tickle
This article presents a support vector machine (SVM) learning approach that adapts class information within the kernel computation. Experiments on fifteen publicly available datasets are conducted and the impact of proposed approach for varied settings are observed. It is noted that the new approach generally improves minority class prediction, depicting it as a well-suited scheme for imbalanced data. However, a SVM based customization is also developed that significantly improves prediction performance in terms of different measures. Overall, the proposed method holds promise with potential for future extensions.

History

Parent Title

ICONIP 2010 : Neural information processing : theory and algorithms, 17th international conference, proceedings, part II, 20-25 November 2010, Sydney, Australia

Start Page

116

End Page

123

Number of Pages

8

Start Date

01/01/2010

Finish Date

01/01/2010

eISSN

1611-3349

ISSN

0302-9743

ISBN-13

9783642175336

Location

Sydney, Australia

Publisher

Springer

Place of Publication

Heidelberg, Germany

Peer Reviewed

Yes

Open Access

No

Era Eligible

Yes

Name of Conference

ICONIP (Conference)