Optimization of stacking ensemble configurations through Artificial Bee Colony algorithm

A Classifier Ensemble combines a finite number of classifiers of same kind or different, trained simultaneously for a common classification task. The Ensemble efficiently improves the generalization ability of the classifier compared to a single classifier. Stacking is one of the most influential ensemble techniques that applies a two level structure of classification namely the base classifiers level and the meta-classifier level. Finding suitable configuration of base level classifiers and the meta-level classifier is always a tedious task and it is domain specific. The Artificial Bee Colony (ABC) Algorithm is a relatively new and popular meta-heuristic search algorithm proved to be successful in solving optimization problems. In this work, we propose the construction of two types of stacking using ABC algorithm: ABC-Stacking1 and ABC-Stacking2. The proposed ABC based stacking is tested using 10 benchmark datasets. The results show that the ABC-Stacking yields promising results and is most useful in selecting the optimal base classifiers configuration and the meta-classifier.