Using a unified measure function for heuristics, discretization, and rule quality evaluation in Ant-Miner

Abstract

Ant-Miner is a classification rule discovery algorithm that is based on Ant Colony Optimization (ACO) metaheuristic. cAnt-Miner is the extended version of the algorithm that handles continuous attributes on-the-fly during the rule construction process, while μAnt-Miner is an extension of the algorithm that selects the rule class prior to its construction, and utilizes multiple pheromone types, one for each permitted rule class. In this paper, we combine these two algorithms to derive a new approach for learning classification rules using ACO. The proposed approach is based on using the measure function for 1) computing the heuristics for rule term selection, 2) a criteria for discretizing continuous attributes, and 3) evaluating the quality of the constructed rule for pheromone update as well. We explore the effect of using different measure functions for on the output model in terms of predictive accuracy and model size. Empirical evaluations found that hypothesis of different functions produce different results are acceptable according to Friedman's statistical test.