An ant colony optimization approach for stacking ensemble

an ensemble in data mining is the strategy that combines a set of different classifiers together to generate an integrated classification system to classify new instances. In the early research, an ensemble outperforms any of its individual components. Stacking is one of the most influential ensemble among the proposed ensemble schemes. Stacking applies a two-level structure: the base-level classifiers output their own predictions and the meta-level classifier takes the outputs as its input to generate final decision. Most of the existing studies focus on the meta-level classifier adoption, and few on the topic about determining the configuration of both base-level classifiers and the meta-level classifier together. This work is inspired by the Ant Colony Optimization which is good at solving combinatorial optimization problems. We propose an ACO-Stacking ensemble approach and also perform some preliminary experiments to compare our approach with some well-known ensembles. The preliminary results show that the performance of the ACO-Stacking is promising.