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

Abstract:

Attribute subset selection is an important issue in dataminingandinformationprocessing.However,mostautomatic 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, we propose 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 de？ned and 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, signi？cance measures are developed to evaluate the approximation quality and classi？cation abilityofIFrelations.Furthermore,aforwardheuristicalgorithm for ？nding one optimal reduct of IF information systems is developed using these measures. Finally, numerical experiments are conducted on public datasets to examine the effectiveness and ef？ciencyoftheproposedalgorithmintermsofthenumberofselected attributes, computational time, and classi？cation accuracy.

Keywords：

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

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Fri Sep 06 21:10:00 CST 2019