Journal article
MAPLSC: A novel multi-class classifier for medical diagnosis
International journal of data mining and bioinformatics, v 5(4), pp 383-401
01 Jan 2011
PMID: 21954671
Featured in Collection : UN Sustainable Development Goals @ Drexel
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.
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Details
- Title
- MAPLSC: A novel multi-class classifier for medical diagnosis
- Creators
- Mingyu You - 1 The Key Laboratory of Embedded System and Service Computing, Ministry of Education, Department of Control Science and Engineering, Tongji University, Shanghai 201804, ChinaRui-Wei Zhao - 2 The Key Laboratory of Embedded System and Service Computing, Ministry of Education, Department of Control Science and Engineering, Tongji University, Shanghai 201804, ChinaGuo-Zheng Li - 3 The Key Laboratory of Embedded System and Service Computing, Ministry of Education, Department of Control Science and Engineering, Tongji University, Shanghai 201804, ChinaXiaohua Hu - 4 College of Information Science and Technology, Drexel University, Philadelphia, PA 19104, USA
- Publication Details
- International journal of data mining and bioinformatics, v 5(4), pp 383-401
- Publisher
- Inderscience Publishers
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000295021800004
- Scopus ID
- 2-s2.0-79960941967
- Other Identifier
- 991014878001304721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Mathematical & Computational Biology