GBFRS: Robust Fuzzy Rough Sets via Granular-ball Computing

TL;DR

This paper introduces a robust fuzzy rough set model enhanced with multi-granularity granular-ball computing, improving efficiency and noise resistance for high-dimensional data analysis.

Contribution

It integrates granular-ball computing into fuzzy rough sets, replacing sample points with granular-balls for increased robustness and scalability in feature selection.

Findings

Model outperforms baseline methods in experiments.

Enhanced robustness against noise in high-dimensional data.

Scalable to large datasets with improved efficiency.

Abstract

Fuzzy rough set theory is effective for processing datasets with complex attributes, supported by a solid mathematical foundation and closely linked to kernel methods in machine learning. Attribute reduction algorithms and classifiers based on fuzzy rough set theory exhibit promising performance in the analysis of high-dimensional multivariate complex data. However, most existing models operate at the finest granularity, rendering them inefficient and sensitive to noise, especially for high-dimensional big data. Thus, enhancing the robustness of fuzzy rough set models is crucial for effective feature selection. Muiti-garanularty granular-ball computing, a recent development, uses granular-balls of different sizes to adaptively represent and cover the sample space, performing learning based on these granular-balls. This paper proposes integrating multi-granularity granular-ball computing into fuzzy rough set theory, using granular-balls to replace sample points. The coarse-grained characteristics of granular-balls make the model more robust. Additionally, we propose a new method for generating granular-balls, scalable to the entire supervised method based on granular-ball computing. A forward search algorithm is used to select feature sequences by defining the correlation between features and categories through dependence functions. Experiments demonstrate the proposed model's effectiveness and superiority over baseline methods.