Random forests have proved to be very effective classifiers, which can achieve very high accuracies. Although a number of papers have discussed the use of fuzzy sets for coping with uncertain data in decision tree learning, fuzzy random forests have not been particularly investigated in the fuzzy community. In this paper, we first propose a simple method for generating fuzzy decision trees by creating fuzzy partitions for continuous variables during the learning phase. Then, we discuss how the method can be used for generating forests of fuzzy decision trees. Finally, we show how these fuzzy random forests achieve accuracies higher than two fuzzy rule-based classifiers recently proposed in the literature. Also, we highlight how fuzzy random forests are more tolerant to noise in datasets than classical crisp random forests.
['Adriano Donato De Matteis', 'Francesco Marcelloni', 'Armando Segatori']
A new approach to fuzzy random forest generation