An ensemble method based on evolving classifiers: eStacking
José Antonio Iglesias, Agapito Ledezma, Araceli Sanchis · 2014
An ensemble can be defined as a set of separately trained classifiers whose predictions are combined in order to achieve better accuracy. It is proved that ensemble methods improve the performance of individual classifiers as long as the members of the ensemble are sufficiently diverse. Much research has been done using different approaches in order to obtain successful ensembles. One of the most used techniques for combining classifiers and improving prediction accuracy is stacking. In this paper, we present a schema based on the stacked generalization. The main contribution of this research is that the base-classifiers of the proposed schema are self-developing (evolving) Fuzzy-rule-based (FRB) classifiers. Since the proposed stacking schema is based on evolving classifiers, it keeps the properties of the evolving classifiers of streaming data. Several versions of this proposed schema have been successfully tested and their results have been extensively analyzed.