Abstract
Multiple classifier systems (MCSs), or simply classifier ensembles, which combine the outputs of a set of base classifiers, have been recently emerged as a method to develop a more accurate classification system. There are two fundamental issues relating to constructing an ensemble of classifiers. The first one is how to construct a set of the base classifiers in such a way that their ensemble can be a successful one; and the second is how to combine a set of base classifiers. This paper deals with the first important issue of ensemble creation. In the paper, a new method for combining classifiers is proposed. The main idea is heuristic retraining of classifiers. Specifically, in the new method named Combinational Classifiers using Heuristic Retraining (CCHR) which proposes a new way for generating diversity in ensemble pool, a classifier is first run, then, focusing on the drawbacks of this base classifier, other classifiers are retrained heuristically. Experimental results show that the MCSs using the proposed method as the constructor of ensemble components outperform those using those using another method as the constructor of ensemble components in terms of testing accuracy.
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Parvin, H., Alizadeh, H., Parvin, S., Maleki, B. (2012). A Heuristic Diversity Production Approach. In: Murgante, B., et al. Computational Science and Its Applications – ICCSA 2012. ICCSA 2012. Lecture Notes in Computer Science, vol 7335. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31137-6_49
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DOI: https://doi.org/10.1007/978-3-642-31137-6_49
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