@InProceedings{MohammadAliBagheri2014,
author="Mohammad Ali Bagheri
and Qigang Gao
and Sergio Escalera",
title="Generic Subclass Ensemble: A Novel Approach to Ensemble Classification",
booktitle="22nd International Conference on Pattern Recognition",
year="2014",
pages="1254--1259",
abstract="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.",
optnote="HuPBA;MILAB",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=2445), last updated on Mon, 03 Jun 2024 08:38:47 +0200",
issn="1051-4651",
doi="10.1109/ICPR.2014.225",
file=":http://refbase.cvc.uab.es/files/BGE2014b.pdf:PDF"
}