Lexicographic multi-objective evolutionary induction of decision trees

Among the several tasks that evolutionary algorithms have successfully employed, the

induction of classification rules and decision trees has been shown to be a relevant approach for

several application domains. Decision tree induction algorithms represent one of the most

popular techniques for dealing with classification problems. However, conventionally used

decision trees induction algorithms present limitations due to the strategy they usually

implement: recursive top-down data partitioning through a greedy split evaluation. The main

problem with this strategy is quality loss during the partitioning process, which can lead to

statistically insignificant rules. In this paper, we propose a new GA-based algorithm for decision

tree induction. The proposed algorithm aims to prevent the greedy strategy and to avoid

converging to local optima. For such, it is based on a lexicographic multi-objective approach. In

order to evaluate the proposed algorithm, it is compared with a well-known and frequently used

decision tree induction algorithm using different public datasets. According to the experimental

results, the proposed algorithm is able to avoid the previously described problems, reporting

accuracy gains. Even more important, the proposed algorithm induced models with a

significantly reduction in the complexity considering tree sizes.