Classification rule mining using ant programming guided by grammar with multiple Pareto fronts
This paper proposes a multi-objective ant programming algorithm for mining classification rules, MOGBAP, which focuses on optimizing sensitivity, specificity, and comprehensibility. It defines a context-free grammar that restricts the search space and ensures the creation of valid individuals, and its heuristic function presents two complementary components. Moreover, the algorithm addresses the classification problem from a new multi-objective perspective specifically suited for this task, which finds an independent Pareto front of individuals per class, so that it avoids the overlapping problem that appears when measuring the fitness of individuals from different classes. A comparative analysis of MOGBAP using two and three objectives is performed, and then its performance is experimentally evaluated throughout 15 varied benchmark data sets and compared to those obtained using another eight relevant rule extraction algorithms. The results prove that MOGBAP outperforms the other algorithms in predictive accuracy, also achieving a good trade-off between accuracy and comprehensibility.
classification ant programming (ap) grammar-based automatic programming multi-objective ant colony optimization (moaco) data mining (dm)