Ensemble classifier system based on ant colony algorithm and its application in chemical pattern classification

A novel ant colony algorithm, mass recruitment and group recruitment based continuous ant colony optimization (MG-CACO), is proposed to solve continuous optimization problems. MG-CACO, which can capture the interdependencies between attributes and does not need discretization as a preprocessing step for optimization, was applied to extract classification rules from samples. To improve the predictive performance of the classifier, the ensemble strategy was adopted and the MG-CACO based ensemble classifier system called MG-CACO-ECS was built. Several datasets, obtained from UCI (University of California, Irvine) machine learning repository, were employed to illustrate the validity of MG-CACO-ECS. The results indicated that MG-CACO-ECS has satisfactory prediction accuracy. Furthermore, the problem of the producing area discrimination of olive oil was studied, and the obtained results demonstrated that MG-CACO-ECS has better prediction accuracy than the reported results.