A Framework towards the Unification of Ensemble Classification Methods

Multiple classifier systems, also known as classifier ensembles, have received great attention in recent years because of the improved classification accuracy in different applications. A large variety of ensemble methods have been proposed in order to exploit strengths of individual classifiers. In this paper, we present a unifying framework for multiple classifier systems, which unites most classification methods by an ensemble of classifiers. Specifically, we link two research lines in machine learning: multiclass classification based on the class binarization techniques and the strategies of ensemble classification. With the proposed framework, the various ensemble classification strategies will be broadly categorized into four main approaches. Then, we provide a brief survey of ensemble methods based on these main approaches as well as principle techniques proposed to combine them.