Title: MAPLSC: A novel multi-class classifier for medical diagnosis

Authors: Mingyu You, Rui-Wei Zhao, Guo-Zheng Li, Xiaohua Hu

Addresses: 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. ' College of Information Science and Technology, Drexel University, Philadelphia, PA 19104, USA

Abstract: Analysis of clinical records contributes to the Traditional Chinese Medicine (TCM) experience expansion and techniques promotion. More than two diagnostic classes (diagnostic syndromes) in the clinical records raise a popular data mining problem: multi-value classification. In this paper, we propose a novel multi-class classifier, named Multiple Asymmetric Partial Least Squares Classifier (MAPLSC). MAPLSC attempts to be robust facing imbalanced data distribution in the multi-value classification. Elaborated comparisons with other seven state-of-the-art methods on two TCM clinical datasets and four public microarray datasets demonstrate MAPLSC|s remarkable improvements.

Keywords: multi-class classification; imbalanced problems; TCM; traditional Chinese medicine; MAPLSC; multiple asymmetric partial least squares classifier; posterior probability output.

DOI: 10.1504/IJDMB.2011.041555

International Journal of Data Mining and Bioinformatics, 2011 Vol.5 No.4, pp.383 - 401

Published online: 26 Jul 2011 *

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