An asymmetric classifier based on partial least squares

Authors:
Hai-Ni Qu;Guo-Zheng Li;Wei-Sheng Xu
Affiliations:
The Key Laboratory of Embedded System and Service Computing, Ministry of Education, Department of Control Science and Engineering, Tongji University, Shanghai 201804, China;The Key Laboratory of Embedded System and Service Computing, Ministry of Education, Department of Control Science and Engineering, Tongji University, Shanghai 201804, China;The Key Laboratory of Embedded System and Service Computing, Ministry of Education, Department of Control Science and Engineering, Tongji University, Shanghai 201804, China
Venue:
Pattern Recognition
Year:
2010

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Abstract

This paper investigates the effect of partial least squares (PLS) in unbalanced pattern classification. Beyond dimension reduction, PLS is proved to be superior to generate favorable features for classification. The PLS classifier (PLSC) is illustrated to give extremely better prediction accuracy to the class with the smaller data number. In this paper, an asymmetric PLS classifier (APLSC) is proposed to boost the poor performance of PLSC to the class with the larger data number. PLSC and APLSC are compared with five state-of-arts algorithms, support vector machines (SVMs), unbalanced SVMs, asymmetric principal component and discriminant analysis (APCDA), SMOTE and Adaboost. Experimental results on six UCI data sets show that APLSC improves PLSC in promoting overall classification accuracy, at the same time, APLSC and PLSC perform better than other five algorithms even under seriously unbalanced distribution.