Investigating the impact of various classification quality measures in the predictive accuracy of ABC-Miner

Abstract

Learning classifiers from datasets is a central problem in data mining and machine learning research. ABC-Miner is an Ant-based Bayesian Classification algorithm that employs the Ant Colony Optimization (ACO) meta-heuristics to learn the structure of Bayesian Augmented Naive-Bayes (BAN) Classifiers. One of the most important aspects of the ACO algorithm is the choice of the quality measure used to evaluate a candidate solution to update pheromone. In this paper, we explore the use of various classification quality measures for evaluating the BAN classifiers constructed by the ants. The aim of this investigation is to discover how the use of different evaluation measures affects the quality of the output classifier in terms of predictive accuracy. In our experiments, we use 6 different classification measures on 25 benchmark datasets. We found that the hypothesis that different measures produce different results is acceptable according to the Friedman's statistical test.