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Fuzzy rough sets and fuzzy rough neural networks for feature selection: A review

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Abstract Feature selection aims to select a feature subset from an original feature set based on a certain evaluation criterion. Since feature selection can achieve efficient feature reduction, it has become a key method for data preprocessing in many data mining tasks. Recently, many feature selection strategies have been developed since in most cases it is infeasible to obtain an optimal/reduced feature subset by using exhaustive search. Among these strategies, fuzzy rough set theory has proved to be an ideal candidate for dealing with uncertain information. This article provides a comprehensive review on the fuzzy rough set theory and two fuzzy rough set theory based feature selection methods, that is, fuzzy rough set based feature selection methods and fuzzy rough neural network based feature selection methods. We review the publications related to the fuzzy rough theory and its applications in feature selection. In addition, the challenges in the two types of feature selection methods are also discussed. This article is categorized under: Technologies > Machine Learning
Rough approximations. Given a universe U, an equivalence relation R can be represented as the dotted line that divides U. If A is a rough set (the area inside the orange circle), then the area inside the green line is the lower approximation of A; the area inside the blue line is the upper approximation of A; the area between the blue and green lines is the boundary region of A
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