Unified Feature Selection Approach for Complex Data Based on Fuzzy β-Covering Reduction via Information Granulation

Xiongtao Zou , Jianhua Dai

IEEE Transactions on Neural Networks and Learning Systems +

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April 29, 2025

View abstract on PubMed

Summary

This study introduces a unified feature selection (UFS) method for complex datasets. The approach effectively handles diverse data types and missing values, outperforming existing methods in machine learning tasks.

Area Of Science

Data Mining and Machine Learning
Artificial Intelligence
Information Theory

Background

Feature selection is crucial for dimensionality reduction, model performance, and interpretability in data analysis.
Existing methods often fail with complex real-world data, such as mixed feature types and missing values.
A gap exists in feature selection techniques capable of handling heterogeneous and incomplete datasets.

Purpose Of The Study

To propose a unified feature selection (UFS) approach for complex data environments.
To develop novel uncertainty measures for fuzzy beta-covering.
To address limitations of current feature selection methods in handling diverse data complexities.

Main Methods

Construction of monotonic uncertainty measures for fuzzy beta-covering from algebraic and information-theoretic perspectives.
Design of two forward heuristic algorithms for fuzzy beta-covering reduction.
Representation of complex, multi-feature data using fuzzy beta-covering via information granulation.

Main Results

The proposed UFS approach effectively handles complex data with multiple feature types and missing values.
Experimental results demonstrate the superiority of the UFS approach compared to 12 state-of-the-art feature selection methods.
The method shows significant improvements in performance and interpretability for machine learning models.

Conclusions

The developed UFS approach provides a robust solution for feature selection in complex data scenarios.
The novel fuzzy beta-covering reduction techniques offer a powerful framework for data analysis.
This work advances the field of feature selection by enabling effective processing of real-world, heterogeneous datasets.