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Multi-label feature selection based on HSIC and sparrow search algorithm.

Tinghua Wang1, Huiying Zhou1, Hanming Liu1

  • 1School of Mathematics and Computer Science, Gannan Normal University, Ganzhou 341000, China.

Mathematical Biosciences and Engineering : MBE
|September 7, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel multi-label feature selection method using the Hilbert-Schmidt independence criterion (HSIC) and sparrow search algorithm (SSA). The approach effectively addresses the curse of dimensionality in multi-label learning tasks.

Keywords:
Hilbert-Schmidt independence criterion (HSIC)data miningfeature selectionmulti-label classificationsparrow search algorithm

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Area of Science:

  • Machine Learning
  • Data Mining
  • Artificial Intelligence

Background:

  • Multi-label learning involves datasets where each sample has multiple associated labels.
  • These labels often exhibit interdependencies, complicating analysis.
  • The

Purpose of the Study:

  • To propose an effective multi-label feature selection method.
  • To address the challenge of the

Main Methods:

  • Utilizes the sparrow search algorithm (SSA) for efficient feature subset searching.
  • Employs the Hilbert-Schmidt independence criterion (HSIC) as the core criterion.
  • HSIC quantifies the dependence between features and all labels to guide selection.

Main Results:

  • Experimental validation confirms the efficacy of the proposed method.
  • The approach successfully identifies optimal feature subsets.
  • Demonstrates significant improvements in handling multi-label data.

Conclusions:

  • The proposed HSIC and SSA-based method is effective for multi-label feature selection.
  • Offers a robust solution to the curse of dimensionality in this domain.
  • Provides a valuable tool for machine learning practitioners.