ABC-based stacking method for multilabel classification

Abstract

Multilabel classification is a supervised learning problem wherein each individual instance is associated with multiple labels. Ensemble methods are effective in managing multilabel classification problems by creating a set of accurate, diverse classifiers and then combining their outputs to produce classifications. This paper presents a novel stacking-based ensemble algorithm, ABC-based stacking, for multilabel classification. The artificial bee colony algorithm, along with a single-layer artificial neural network, is used to find suitable meta-level classifier configurations. The optimization goal of the meta-level classifier is to maximize the average accuracy of classification of all the instances involved. We run an experiment on 10 benchmark datasets of varying domains and compare the proposed approach to five other ensemble algorithms to demonstrate the feasibility and effectiveness of ABC-based stacking.