CONSTRUCTING FUZZY PARTITIONS FROM IMPRECISE DATA

Classification is an important task in Data Mining. In order to carry out classification, many classifiers require a previous preparatory step for their data. In this paper we focus on the process of discretization of attributes because this process is a very important part in Data Mining. In many situations, the values of the attributes present imprecision because imperfect information inevitably appears in real situations for a variety of reasons. Although, many 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. Therefore, in this paper we propose an algorithm to construct fuzzy partitions from imprecise information and we evaluate them in a Fuzzy Random Forest ensemble which is able to work with imprecise information too. Also, we compare our proposal with results of other works.