Classificação com algoritmo AdaBoost.M1 : o mito do limiar de erro de treinamento

Abstract

The accelerated growth of data repositories, in the different areas of activity, opens space for research in the area of data mining, in particular, with the methods of classification and combination of classifiers. The Boosting method is one of them, which combines the results of several classifiers in order to obtain better results. The main purpose of this dissertation is the experimentation of alternatives to increase the effectiveness and performance of the algorithm AdaBoost.M1, which is the implementation often employed by the Boosting method. An empirical study was perfered taking into account stochastic aspects trying to shed some light on an obscure internal parameter, in which algorithm creators and other researchers assumed that the training error threshold should be correlated with the number of classes in the target data set and logically, most data sets should use a value of 0.5. In this paper, we present an empirical evidence that this is not a fact, but probably a myth originated by the mistaken application of the theoretical assumption of the joint effect. To achieve this goal, adaptations were proposed for the algorithm, focusing on finding a better suggestion to define this threshold in a general case.