Inducing decision trees with an ant colony optimization algorithm

Decision trees have been widely used in data mining and machine learning as a comprehensible knowledge representation. While ant colony optimization (ACO) algorithms have been successfully applied to extract classification rules, decision tree induction with ACO algorithms remains an almost unexplored research area. In this paper we propose a novel ACO algorithm to induce decision trees, combining commonly used strategies from both traditional decision tree induction algorithms and ACO. The proposed algorithm is compared against three decision tree induction algorithms, namely C4.5, CART and cACDT, in 22 publicly available data sets. The results show that the predictive accuracy of the proposed algorithm is statistically significantly higher than the accuracy of both C4.5 and CART, which are well-known conventional algorithms for decision tree induction, and the accuracy of the ACO-based cACDT decision tree algorithm.

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► We propose an ant colony optimization (ACO) algorithm for decision tree induction.
► The proposed algorithm, called Ant-Tree-Miner, is evaluated on 22 publicly available data sets.
► The results show that the Ant-Tree-Miner algorithm outperforms well-known C4.5 and CART decision tree algorithms, and the ACO-based cACDT decision tree algorithm.