A Survey of Evolutionary Algorithms for Decision-Tree Induction

• Published in: IEEE transactions on systems, man and cybernetics. Part C, Applications and reviews Vol. 42; no. 3; pp. 291 - 312
• Main Authors: Barros, R. C., Basgalupp, M. P., de Carvalho, A. C. P. L. F., Freitas, A. A.
• Format: Journal Article
• Language: English
• Published: New-York, NY IEEE 01.05.2012; Institute of Electrical and Electronics Engineers
• Subjects: Algorithmics. Computability. Computer arithmetics; Applied sciences; Biological cells; Classification; Computer science; control theory; systems; Decision trees; decision-tree induction; Encoding; evolutionary algorithms (EAs); Evolutionary computation; Exact sciences and technology; Genetic algorithms; Genetics; Indexes; regression; soft computing; Theoretical computing; Hierarchical classification; Taxonomy; Evolutionary algorithm; Top down method; decision-tree induction; Soft computing; Phylogeny; Decision tree; evolutionary algorithms (EAs); regression; Heuristic method; Classification; Divide and conquer method
• ISSN: 1094-6977; 1558-2442; 1558-2442
• DOI: 10.1109/TSMCC.2011.2157494

This paper presents a survey of evolutionary algorithms that are designed for decision-tree induction. In this context, most of the paper focuses on approaches that evolve decision trees as an alternate heuristics to the traditional top-down divide-and-conquer approach. Additionally, we present some alternative methods that make use of evolutionary algorithms to improve particular components of decision-tree classifiers. The paper's original contributions are the following. First, it provides an up-to-date overview that is fully focused on evolutionary algorithms and decision trees and does not concentrate on any specific evolutionary approach. Second, it provides a taxonomy, which addresses works that evolve decision trees and works that design decision-tree components by the use of evolutionary algorithms. Finally, a number of references are provided that describe applications of evolutionary algorithms for decision-tree induction in different domains. At the end of this paper, we address some important issues and open questions that can be the subject of future research.