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Towards the learning from low quality data in a Fuzzy Random Forest ensemble

Imperfect information inevitably appears in real situations for a variety of reasons. Although efforts have been made to incorporate imperfect data into classification techniques, there are still many limitations as to the type of data, uncertainty and imprecision that can be handled. In this paper, we will present a Fuzzy Random Forest ensemble for classification and show its ability to handle imperfect data into the learning and the classification phases. Then, we will describe the types of imperfect data it supports. We will devise an augmented ensemble that can operate with others type of imperfect data: crisp, missing, probabilistic uncertainty and imprecise (fuzzy and crisp) values. Additionally, we will perform experiments with datasets used in other papers to show the advantage of being able to express the true nature of imperfect information.