%0 Conference Proceedings
%T Generic Subclass Ensemble: A Novel Approach to Ensemble Classification
%A Mohammad Ali Bagheri
%A Qigang Gao
%A Sergio Escalera
%B 22nd International Conference on Pattern Recognition
%D 2014
%@ 1051-4651
%F Mohammad Ali Bagheri2014
%O HuPBA;MILAB
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=2445), last updated on Mon, 03 Jun 2024 08:38:47 +0200
%X Multiple classifier systems, also known as classifier ensembles, have received great attention in recent years because of their improved classification accuracy in different applications. In this paper, we propose a new general approach to ensemble classification, named generic subclass ensemble, in which each base classifier is trained with data belonging to a subset of classes, and thus discriminates among a subset of target categories. The ensemble classifiers are then fused using a combination rule. The proposed approach differs from existing methods that manipulate the target attribute, since in our approach individual classification problems are not restricted to two-class problems. We perform a series of experiments to evaluate the efficiency of the generic subclass approach on a set of benchmark datasets. Experimental results with multilayer perceptrons show that the proposed approach presents a viable alternative to the most commonly used ensemble classification approaches.
%U http://refbase.cvc.uab.es/files/BGE2014b.pdf
%U http://dx.doi.org/10.1109/ICPR.2014.225
%P 1254-1259