Revisiting evolutionary algorithms in feature selection and nonfuzzy/fuzzy rule based classification
Article first published online: 20 FEB 2013
Copyright © 2013 John Wiley & Sons, Inc.
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Volume 3, Issue 2, pages 83–108, March/April 2013
How to Cite
Dehuri, S. and Ghosh, A. (2013), Revisiting evolutionary algorithms in feature selection and nonfuzzy/fuzzy rule based classification. WIREs Data Mining Knowl Discov, 3: 83–108. doi: 10.1002/widm.1087
- Issue published online: 25 FEB 2013
- Article first published online: 20 FEB 2013
This paper discusses the relevance and possible applications of evolutionary algorithms, particularly genetic algorithms, in the domain of knowledge discovery in databases. Knowledge discovery in databases is a process of discovering knowledge along with its validity, novelty, and potentiality. Various genetic-based feature selection algorithms with their pros and cons are discussed in this article. Rule (a kind of high-level representation of knowledge) discovery from databases, posed as single and multiobjective problems is a difficult optimization problem. Here, we present a review of some of the genetic-based classification rule discovery methods based on fidelity criterion. The intractable nature of fuzzy rule mining using single and multiobjective genetic algorithms reported in the literatures is reviewed. An extensive list of relevant and useful references are given for further research. © 2013 Wiley Periodicals, Inc.