title: MAPLSC: a novel multi-class classifier for medical diagnosis.
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.
authors.affiliation: The Key Laboratory of Embedded System and Service Computing, Ministry of Education, Department of Control Science and Engineering, Tongji University, Shanghai 201804, China. myyou@tongji.edu.cn
authors.forename: Mingyu
authors.initials: M
authors.lastname: You
authors.forename: Rui-Wei
authors.initials: RW
authors.lastname: Zhao
authors.forename: Guo-Zheng
authors.initials: GZ
authors.lastname: Li
authors.forename: Xiaohua
authors.initials: X
authors.lastname: Hu
mesh_terms: D000465:Algorithms; D003933:Diagnosis; D003937:Diagnosis, Differential; D006801:Humans; D016018:Least-Squares Analysis; D008516:Medicine, Chinese Traditional; D020411:Oligonucleotide Array Sequence Analysis / Q000145:classification / Q000379:methods; D012984:Software; D013577:Syndrome