A Bagging algorithm for imprecise classification in cost-sensitive scenarios

Abstract

Classic classification aims to predict the value of a variable under study, also called the class variable, based on an attribute set associated with a given item. When the evidence is not strong enough to determine such a value, a set of values of the class variable probably represents a more informative situation. This is called Imprecise Classification. An important aspect of any classification task is the cost that must be assumed in the case of error. Previous research has shown that the fusion/combination of classifiers tends to obtain better predictive results. In Imprecise Classification, there are very few models capable of efficiently fusing information from multiple classifiers. Concerning Imprecise Classification considering error costs, there is no method for this aim in the literature so far. This work presents the first method capable of fusing imprecise classifiers that take into account error costs. To do this, a procedure representing a midpoint between misclassification risk and informative outputs is used as a basis. Experiments highlight that our proposed fusion procedure shows an improvement in the results over those obtained by other methods of the state-of-the-art.