Generating Fuzzy Partitions from Nominal and Numerical Attributes with Imprecise Values
In areas of Data Mining and Soft Computing is important the discretization of numerical attributes because there are techniques that can not work with numerical domains or can get better results when working with discrete domains. The precision obtained with these techniques depends largely on the quality of the discretization performed. Moreover, in many real-world applications, data from which the discretization is carried out, are imprecise. In this paper we address both problems by proposing an algorithm to obtain a fuzzy discretization of numerical attributes from input data that show imprecise values in both numerical and nominal attributes. To evaluate the proposed algorithm we analyze the results on a set of datasets from different real-world problems.
KeywordsFuzzy partition Imperfect information Fuzzy random forest ensemble Imprecise data
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