Probability granular distance-based fuzzy rough set model

Fuzzy rough set theory is sensitive to noisy samples as the fuzzy approximations are proposed based on sensitive statistics, i.e. minimum and maximum. Here, we develop a robust fuzzy rough set model called probability granular distance-based fuzzy rough sets (PGDFRS), in which the similarity between samples is substituted by that between granules to reduce the impact of noise on the statistical minimum and maximum. The robust principle is to take the probability density values of samples as weights for computing probability distances between granules. By using PGDFRS, a feature selection algorithm is created. This algorithm limits feature selection to two-dimensional space and avoids the difficulty of parameter setting in high-dimensional space. The experimental results indicate that the designed feature selection algorithm is effective and robust. Additionally, it confirms that the proposed PGDFRS model is more robust than some existing fuzzy rough set models. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

The PGDFRS model reduces the impact of noise on statistical minimum and maximum values by introducing the concept of probability granular distance, and it also creates a feature selection algorithm limited to two-dimensional space, avoiding difficulties in parameter setting in high-dimensional space.