PT Unknown
AU Mohammad Ali Bagheri
   Qigang Gao
   Sergio Escalera
TI Generic Subclass Ensemble: A Novel Approach to Ensemble Classification
BT 22nd International Conference on Pattern Recognition
PY 2014
BP 1254
EP 1259
DI 10.1109/ICPR.2014.225
AB 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.
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