Welcome to the InfoSci Platform
Reference Hub1
A Comparative Study of Associative Classifiers in Mesenchymal Stem Cell Differentiation Analysis

A Comparative Study of Associative Classifiers in Mesenchymal Stem Cell Differentiation Analysis

Weiqi Wang, Yanbo J. Wang, Qin Xin, René Bañares-Alcántara, Frans Coenen, Zhanfeng Cui
Copyright: © 2011 |Pages: 21
ISBN13: 9781609600679|ISBN10: 1609600673|EISBN13: 9781609600693
DOI: 10.4018/978-1-60960-067-9.ch011
Cite Chapter Cite Chapter

MLA

Wang, Weiqi, et al. "A Comparative Study of Associative Classifiers in Mesenchymal Stem Cell Differentiation Analysis." Knowledge Discovery Practices and Emerging Applications of Data Mining: Trends and New Domains, edited by A.V. Senthil Kumar, IGI Global, 2011, pp. 223-243. https://doi.org/10.4018/978-1-60960-067-9.ch011

APA

Wang, W., Wang, Y. J., Xin, Q., Bañares-Alcántara, R., Coenen, F., & Cui, Z. (2011). A Comparative Study of Associative Classifiers in Mesenchymal Stem Cell Differentiation Analysis. In A. Kumar (Ed.), Knowledge Discovery Practices and Emerging Applications of Data Mining: Trends and New Domains (pp. 223-243). IGI Global. https://doi.org/10.4018/978-1-60960-067-9.ch011

Chicago

Wang, Weiqi, et al. "A Comparative Study of Associative Classifiers in Mesenchymal Stem Cell Differentiation Analysis." In Knowledge Discovery Practices and Emerging Applications of Data Mining: Trends and New Domains, edited by A.V. Senthil Kumar, 223-243. Hershey, PA: IGI Global, 2011. https://doi.org/10.4018/978-1-60960-067-9.ch011

Export Reference

Mendeley
Favorite

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.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.