Adapting a Fuzzy Random Forest for Ordinal Multi-Class Classification

Abstract

Fuzzy Random Forests are well-known Machine Learning ensemble methods. They combine the outputs of multiple Fuzzy Decision Trees to improve the classification performance. Moreover, they can deal with data uncertainty and imprecision thanks to the use of fuzzy logic. Although many classification tasks are binary, in some situations we face the problem of classifying data into a set of ordered categories. This is a particular case of multi-class classification where the order between the classes is relevant, for example in medical diagnosis to detect the severity of a disease. In this paper, we explain how a binary Fuzzy Random Forest may be adapted to deal with ordinal classification. The work is focused on the prediction stage, not on the construction of the fuzzy trees. When a new instance arrives, the rules activation is done with the usual fuzzy operators, but the aggregation of the outputs given by the different rules and trees has been redefined. In particular, we present a procedure for managing the conflicting cases where different classes are predicted with similar support. The support of the classes is calculated using the OWA operator that permits to model the concept of majority agreement.

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