Authors: Anhui Tan, Weizhi Wu, Yuhua Qian, Jiye Liang, Jinkun Chen, Jinjin Li

Abstract:

Authors:Anhui Tan, Weizhi Wu, Yuhua Qian, Jiye Liang, Jinkun Chen, Jinjin Li
Abstract:Attribute subset selection is an important issue in data mining and information processing. However, most automatic methodologies consider only the relevance factor between samples while ignoring the diversity factor. This may not allow the utilization value of hidden information to be exploited. For this reason, in this study, we employ a hybrid model named intuitionistic fuzzy (IF) rough set to overcome this limitation. The model combines the technical advantages of rough set and IF set and can effectively consider the above-mentioned statistical factors. First, fuzzy information granules based on IF relations are defined, which are used to characterize the hierarchical structures of the lower and upper approximations of IF rough set within the framework of granular computing. Then, the computation of IF rough approximations and knowledge reduction in IF information systems are investigated.Third,based on the approximations of IF rough set, signicance measures are developed to evaluate the approximation quality and classification ability of IF relations. Furthermore, a forward heuristic algorithm for finding one optimal reduct of IF information systems is developed using these measures. Finally, numerical experiments are conducted on public data sets to examine the effectiveness and efficiency of the proposed algorithm in terms of the number of selected attributes, computational time, and classification accuracy.

Intuitionistic fuzzy rough set-based granular structures and attribute subset selection

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intuitionistic fuzzy rough set-based granular.pdf

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Sat Aug 25 00:00:00 CST 2018