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Granular Ball-Based Noise-Resistant Fuzzy Multineighborhood Feature Selection via Label Enhancement and Feature

Lin Sun, Wenjuan Du, Weiping Ding

IEEE Transactions on Neural Networks and Learning Systems

|July 2, 2026

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This study introduces a novel feature selection method for multilabel data, enhancing label descriptions and reducing noise. The approach improves classification accuracy by exploring feature interactions and label complementarity.

Area of Science:

Machine Learning
Data Mining
Pattern Recognition

Background:

Increasing volume of multilabel data presents challenges in feature selection.
Existing methods often overlook feature interactions, label complementarity, and descriptive differences.
Noise and abundant features adversely affect classification efficacy in multilabel datasets.

Purpose of the Study:

To develop a granular-ball-based, noise-resistant fuzzy multineighborhood feature selection scheme.
To leverage label enhancement and a feature graph for improved multilabel feature selection.
To address the limitations of existing methods in exploring feature interactions and label descriptiveness.

Main Methods:

Constructed a granular-ball-based, noise-resistant fuzzy multineighborhood rough set model.

Related Experiment Videos

Last Updated: Jul 4, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Developed sample similarity using label Jaccard similarity and Pearson correlation coefficient.

Designed adaptive fuzzy multineighborhood granules and achieved label enhancement via two-space fusion.

Main Results:

Derived uncertainty measures to study relevance, association, redundancy, complementarity, and interactivity.
Utilized multiple correlation relationships to develop a weighted feature graph for feature significance evaluation.
Demonstrated superior performance over state-of-the-art methods on 14 datasets.

Conclusions:

The proposed scheme effectively addresses challenges in multilabel feature selection.
The method enhances label descriptions and explores feature interactions and complementarity.
The granular-ball-based, noise-resistant approach significantly improves classification efficacy.