Details

Autor(en) / Beteiligte

• Qu, Hai-Ni
• Li, Guo-Zheng
• Xu, Wei-Sheng

Titel

An asymmetric classifier based on partial least squares

Ist Teil von

• Pattern recognition, 2010-10, Vol.43 (10), p.3448-3457

Ort / Verlag

Kidlington: Elsevier Ltd

Erscheinungsjahr

2010

Quelle

ScienceDirect

Beschreibungen/Notizen

• 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.