Output details
11 - Computer Science and Informatics
University of Kent
Inducing decision trees with an ant colony optimization algorithm
<24> This paper proposes a second ant colony optimization (ACO) classification algorithm for decision tree induction and is third by this group in this area. Decision trees are widely used as a comprehensible representation model, given that they can be represented in a graphical form as well as a set of classification rules. The paper compares the proposed algorithm against well-know decision tree induction algorithms – namely C4.5, CART, and the ACO-based cACDT – in an extensive empirical evaluation. The results show that the predictive accuracy of the proposed algorithm is statistically significantly higher than the accuracy of the others.