Abstract
Feature Subset Selection Based on Variable Precision Neighborhood Rough Sets
Highlights
The classical Rough Set Theory (RST) proposed by Pawlak [1] refers to the whole study objects as a domain
(3) we prove the monotonicity of variable precision neighborhood dependence with increasing features and a feature subset selection algorithm to the variable precision neighborhood rough sets is designed
According to the definition of the variable precision neighborhood reduction, we propose a feature subset selection algorithm based on the variable precision neighborhood rough sets
Summary
The classical Rough Set Theory (RST) proposed by Pawlak [1] refers to the whole study objects as a domain. Based on the granule inclusion degree, we construct neighborhood variable precision positive region sets and the significance of a feature. A feature selection algorithm based on variable precision neighborhood granulation is further designed, and some UCI datasets are applied to test the classification performance of the selected feature subsets. The main innovative work of this paper is as follows: (1) In order to enhance the fault-tolerant ability of classification systems, we define some concepts of granule inclusion, variable precision neighborhood dependence and approximation sets. (2) Based on these terminologies, a variable precision neighborhood rough set model is proposed, which is suitable to deal with real-value and noisy data.
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