A study on the selection of local training sets for hierarchical classification tasks

Jean MetzAlex A. FreitasMaria Carolina MonardEverton Alvares Cherman

In hierarchical classification tasks using the local approach, an im- portant decision concerns the selection of training examples to build the local classifiers. To this end, several policies, which take into account the class tax- onomy information, have been proposed. However, a study of a comprehensive comparison concerning the performance of these policies is still lacking. This paper presents a comprehensive empirical evaluation of eight different policies using 13 datasets. The results have shown that three of these policies outper- formed the other five policies with statistically significant differences.

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