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A Comparative Study of Associative Classifiers in Mesenchymal Stem Cell Differentiation Analysis
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Author(s): Weiqi Wang (University of Oxford, UK), Yanbo J. Wang (China Minsheng Banking Corporation Ltd., China), Qin Xin (Simula Research Laboratory, Norway), René Bañares-Alcántara (University of Oxford, UK), Frans Coenen (University of Liverpool, UK)and Zhanfeng Cui (University of Oxford, UK)
Copyright: 2013
Pages: 21
Source title:
Data Mining: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-4666-2455-9.ch049
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Abstract
Discovering how Mesenchymal Stem Cells (MSCs) can be differentiated is an important topic in stem cell therapy and tissue engineering. In a general context, such differentiation analysis can be modeled as a classification problem in data mining. Specifically, this is concerned with the single-label multi-class classification task. Previous studies on this topic suggests the Associative Classification (AC) rather than other alternative (Classification) techniques, and presented classification results based on the CMAR (Classification based on Multiple Association Rules) associative classifier. Other AC algorithms include: CBA (Classification Based on Associations), PRM (Predictive Rule Mining), CPAR (Classification based on Predictive Association Rules) and TFPC (Total From Partial Classification). The main aim of this chapter is to compare the performance of different associative classifiers, in terms of classification accuracy, efficiency, number of rules to be generated, quality of such rules, and the maximum number of attributes in rule-antecedents, with respect to MSC differentiation analysis.
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