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Proceedings Paper

Imbalanced data classification using reduced multivariate polynomial
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Paper Abstract

In this paper, a weighted reduced multivariate polynomial for class imbalance learning is proposed. When there is a large variation in the numbers of available class samples, class distribution is said to be imbalanced. In such cases, conventional classifiers may classify most samples as majority classes to maximize the classification accuracy, which may not be desirable in some applications. Thus, for imbalanced data classification, an additional algorithm may be required to address low representation of minority classes when the classification performance of those classes is important. We used weighted ridge regression for class imbalanced data classification. Experimental results with the UCI database show improved classification of the minority classes.

Paper Details

Date Published: 19 May 2016
PDF: 8 pages
Proc. SPIE 9874, Remotely Sensed Data Compression, Communications, and Processing XII, 98740N (19 May 2016); doi: 10.1117/12.2224452
Show Author Affiliations
Seongyoun Woo, Yonsei Univ. (Korea, Republic of)
Chulhee Lee, Yonsei Univ. (Korea, Republic of)

Published in SPIE Proceedings Vol. 9874:
Remotely Sensed Data Compression, Communications, and Processing XII
Bormin Huang; Chein-I Chang; Chulhee Lee, Editor(s)

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