Investigating Evaluation Measures in Ant Colony Algorithms for Learning Decision Tree Classifiers

Classification is a data mining task where the goal is to build, from labeled cases, a model that can be used to predict the class of unlabeled cases. Ant-Tree-Miner is a decision tree induction algorithm that is based on the Ant Colony Optimization (ACO) meta-heuristic. Ant-Tree-Miner_M is a recently introduced adaptation of the ACO algorithm that learns multi-tree classification models. A multi-tree model consists of multiple decision trees, one for each class value, where each class-based decision tree is responsible for discriminating between its class value and all other values present in the class domain (one vs. All). In this paper, we investigate the use of 10 different classification quality evaluation measures in Ant-Tree-Miner_M, which are used for both candidate model evaluation and model pruning. Our experimental results, using 40 popular benchmark datasets, identify several quality functions that significantly improve on the simple Accuracy quality function that was previously used in Ant-Tree-MinerM.