Boosting in the limit: maximizing the margin of learned ensembles
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
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SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
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When dealing with two-class problems the combination of several dichotomizers is an established technique to improve the classification performance. In this context the margin is considered a central concept since several theoretical results show that improving the margin on the training set is beneficial for the generalization error of a classifier. In particular, this has been analyzed with reference to learning algorithms based on boosting which aim to build strong classifiers through the combination of many weak classifiers. In this paper we try to experimentally verify if the margin maximization can be beneficial also when combining already trained classifiers. We have employed an algorithm for evaluating the weights of a linear convex combination of dichotomizers so as to maximize the margin of the combination on the training set. Several experiments performed on publicly available data sets have shown that a combination based on margin maximization could be particularly effective if compared with other established fusion methods.