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The discretization of values plays a critical role in data mining and knowledge discovery. The representation of information through intervals is more concise and easier to understand at certain levels of knowledge than the representation by mean continuous values. In this paper, we propose a method for discretizing continuous attributes by means of fuzzy sets, which constitute a fuzzy partition of the domains of these attributes. This method carries out a fuzzy discretization of continuous attributes in two stages. A fuzzy decision tree is used in the first stage to propose an initial set of crisp intervals, while a genetic algorithm is used in the second stage to define the membership functions and the cardinality of the partitions. After defining the fuzzy partitions, we evaluate and compare them with previously existing ones in the literature.

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 imperfect datasets created for this purpose and datasets used in other papers to show the advantage of being able to express the true nature of imperfect information.

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