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Author(s): G. Tsoumakas, I. Katakis, I. Vlahavas.
Title: “Effective Voting of Heterogeneous Classifiers”.
Availability:
Click here to download the PDF (Acrobat Reader) file (12 pages).
Keywords:
Voting, Multiple Classifier Systems, Ensemble Methods, Classification.
Appeared in:
Proc. European Conference on Machine Learning, ECML 04, Jean-Francois Boulicaut, Floriana Esposito, Fosca Giannoti, Dino Pedreschi (Ed.), LNAI 3201, pp. 465-476, Pisa, Italy, 2004. Abstract: This paper deals with the combination of classification models
that have been derived from running different (heterogeneous) learning
algorithms on the same data set. We focus on the Classifier Evaluation
and Selection (ES) method, that evaluates each of the models (typically
using 10-fold cross-validation) and selects the best one.We examine
the performance of this method in comparison with the Oracle selecting
the best classifier for the test set and show that 10-fold cross-validation
has problems in detecting the best classifier. We then extend ES by applying
a statistical test to the 10-fold accuracies of the models and combining
through voting the most significant ones. Experimental results
show that the proposed method, Effective Voting, performs comparably
with the state-of-the-art method of Stacking with Multi-Response Model
Trees without the additional computational cost of meta-training.
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