Home
This Title All WIREs
WIREs RSS Feed
How to cite this WIREs title:
WIREs Data Mining Knowl Discov
Impact Factor: 2.541

Revisiting evolutionary algorithms in feature selection and nonfuzzy/fuzzy rule based classification

Full article on Wiley Online Library:   HTML PDF

Can't access this content? Tell your librarian.

Abstract 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. This article is categorized under: Fundamental Concepts of Data and Knowledge > Key Design Issues in Data Mining Technologies > Computational Intelligence

Decision tree.

[ Normal View | Magnified View ]

Approaches for FCRM using GAs.

[ Normal View | Magnified View ]

Browse by Topic

Fundamental Concepts of Data and Knowledge > Key Design Issues in Data Mining
Technologies > Computational Intelligence

Access to this WIREs title is by subscription only.

Recommend to Your
Librarian Now!

The latest WIREs articles in your inbox

Sign Up for Article Alerts