For heterogeneous data sets containing numerical and symbolic feature values, feature selection based on fuzzy neighborhood multigranulation rough sets (FNMRS) is a very significant step to preprocess data and improve its classification performance. This article presents an FNMRS-based feature selection approach in neighborhood decision systems. First, some concepts of fuzzy neighborhood rough sets and neighborhood multigranulation rough sets are given, and then the FNMRS model is investigated to construct uncertainty measures. Second, the optimistic and pessimistic FNMRS models are built by using fuzzy neighborhood multigranulation lower and upper approximations from algebra view, and some fuzzy neighborhood entropy-based uncertainty measures are developed in information view. Inspired by both algebra and information views based on the FNMRS model, the fuzzy neighborhood pessimistic multigranulation entropy is proposed. Third, the Fisher score model is utilized to delete irrelevant features to decrease the complexity of high-dimensional data sets, and then, a forward feature selection algorithm is provided to promote the performance of heterogeneous data classification. Experimental results on 12 data sets show that the presented model is effective for selecting important features with the higher stability of classification in neighborhood decision systems.

Feature Selection Using Fuzzy Neighborhood Entropy-Based Uncertainty Measures for Fuzzy Neighborhood Multigranulation Rough Sets

In this article, a novel fuzzy $\alpha$ -neighborhood operator with reflexivity is constructed and a new fuzzy rough set model based on the fuzzy $\alpha$ -neighborhood operator is proposed. Aiming at decision-making in information systems with real-valued information systems (RVISs), we first utilize data normalization method to effectively transform RVISs into information systems with fuzzy-valued information systems (FVISs). Then, we use the fuzzy $\alpha$ -neighborhood-based fuzzy rough set model to convert FVISs into information systems with intuitionistic fuzzy-valued information systems (IFVISs). By adopting the idea of the PROMETHEE II method, we develop three different sorting decision-making schemes on IFVISs, which consist of the subtraction of intuitionistic fuzzy numbers, sorting functions, and intimacy coefficients. Finally, numerical experiments demonstrate the effectiveness of our method. Comparative studies and Spearman rank correlation analyses explain the superiority of our schemes. Experimental results verify the stability of the performance of our strategy.

On Multicriteria Decision-Making Method Based on a Fuzzy Rough Set Model With Fuzzy $\alpha$ -Neighborhoods

A novel fuzzy rough set model with fuzzy neighborhood operators

A novel decision-making approach based on three-way decisions in fuzzy information systems

Kai Zhang

Papers

Fuzzy β-covering based (I,T)-fuzzy rough set models and applications to multi-attribute decision-making

Novel fuzzy rough set models and corresponding applications to multi-criteria decision-making

On Multicriteria Decision-Making Method Based on a Fuzzy Rough Set Model With Fuzzy $\alpha$ -Neighborhoods

TOPSIS method based on a fuzzy covering approximation space: An application to biological nano-materials selection

TOPSIS-WAA method based on a covering-based fuzzy rough set: an application to rating problem