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

Satchidananda Dehuri, Ashish Ghosh
2013 Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery  
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
more » ... 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. C 2013 Wiley Periodicals, Inc. The subject of KDD has evolved and continues to evolve, from the intersection of research from various fields such as databases, machine learning, 52 pattern recognition, statistics, artificial intelligence, reasoning with uncertainties, knowledge acquisition for expert systems, 53 data visualization, machine discovery, high-performance computing, evolutionary computation, multiobjective evolutionary computation, and swarm intelligence. 12,54
doi:10.1002/widm.1087 fatcat:j3evku467fbifgrkt5fpu5x4im