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Revisiting evolutionary algorithms in feature selection and nonfuzzy/fuzzy rule based classification
Author(s) -
Dehuri Satchidananda,
Ghosh Ashish
Publication year - 2013
Publication title -
wiley interdisciplinary reviews: data mining and knowledge discovery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.506
H-Index - 47
eISSN - 1942-4795
pISSN - 1942-4787
DOI - 10.1002/widm.1087
Subject(s) - computer science , relevance (law) , knowledge extraction , feature selection , artificial intelligence , data mining , novelty , machine learning , evolutionary algorithm , fuzzy rule , fuzzy logic , selection (genetic algorithm) , domain knowledge , k optimal pattern discovery , key (lock) , process (computing) , genetic algorithm , evolutionary computation , feature (linguistics) , fuzzy set , philosophy , theology , computer security , political science , law , operating system , linguistics
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