Evolving decision trees with beam search-based initialization and lexicographic multi-objective evaluation
Data
2014-02-10
Autores
Basgalupp, Marcio P. 
Tipo
Artigo
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Resumo
Decision tree induction algorithms represent one of the most popular techniques for dealing with classification problems. However, traditional decision-tree induction algorithms implement a greedy approach for node splitting that is inherently susceptible to local optima convergence. Evolutionary algorithms can avoid the problems associated with a greedy search and have been successfully employed to the induction of decision trees. Previously, we proposed a lexicographic multi-objective genetic algorithm for decision-tree induction, named LEGAL-Tree. in this work, we propose extending this approach substantially, particularly w.r.t. two important evolutionary aspects: the initialization of the population and the fitness function. We carry out a comprehensive set of experiments to validate our extended algorithm. the experimental results suggest that it is able to outperform both traditional algorithms for decision-tree induction and another evolutionary algorithm in a variety of application domains. (C) 2013 Elsevier Inc. All rights reserved.
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Citação
Information Sciences. New York: Elsevier B.V., v. 258, p. 160-181, 2014.