Evolutionary Induction of Decision Trees for Misclassification Cost Minimization

Abstract. In the paper, a new method of decision tree learning for costsensitive classification is presented. In contrast to the traditional greedy top-down inducer in the proposed approach optimal trees are searched in a global manner by using an evolutionary algorithm (EA). Specialized genetic operators are applied to modify both the tree structure and tests in non-terminal nodes. A suitably defined fitness function enables the algorithm to minimize the misclassification cost instead of the number of classification errors. The performance of the EA-based method is compared to three well-recognized algorithms on real-life problems with known and randomly generated cost-matrices. Obtained results show that the proposed approach is competitive both in terms of misclassification cost and compactness of the classifier at least for some datasets.

• Type: Conference
• Year: 2007
• Where: ICANNGA
• Authors: Marek Kretowski, Marek Grzes
• Keywords: Algorithms, Approach Optimal Trees, Greedy Top-down Inducer, ICANNGA 2007, Misclassification Cost