Submit

Login Login

Register Register

Home / Journals / CMC / Online First / doi:10.32604/cmc.2025.074138
Journal Menu
Special Issues
Table of Content

All issues

Online First

2026

2025

2024

2023

2022

2021

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

Open Access

ARTICLE

A Unified Feature Selection Framework Combining Mutual Information and Regression Optimization for Multi-Label Learning

Hyunki Lim*
Division of AI Computer Science and Engineering, Kyonggi University, Gwanggyosan-Ro, Yeongtong-Gu, Suwon-Si, 16227, Gyeonggi-Do, Republic of Korea
* Corresponding Author: Hyunki Lim. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.074138

Received 03 October 2025; Accepted 27 November 2025; Published online 19 December 2025

Abstract

High-dimensional data causes difficulties in machine learning due to high time consumption and large memory requirements. In particular, in a multi-label environment, higher complexity is required as much as the number of labels. Moreover, an optimization problem that fully considers all dependencies between features and labels is difficult to solve. In this study, we propose a novel regression-based multi-label feature selection method that integrates mutual information to better exploit the underlying data structure. By incorporating mutual information into the regression formulation, the model captures not only linear relationships but also complex non-linear dependencies. The proposed objective function simultaneously considers three types of relationships: (1) feature redundancy, (2) feature-label relevance, and (3) inter-label dependency. These three quantities are computed using mutual information, allowing the proposed formulation to capture nonlinear dependencies among variables. These three types of relationships are key factors in multi-label feature selection, and our method expresses them within a unified formulation, enabling efficient optimization while simultaneously accounting for all of them. To efficiently solve the proposed optimization problem under non-negativity constraints, we develop a gradient-based optimization algorithm with fast convergence. The experimental results on seven multi-label datasets show that the proposed method outperforms existing multi-label feature selection techniques.

Keywords

feature selection; multi-label learning; regression model optimization; mutual information

  • 410

    View

  • 129

    Download

  • 0

    Like

Related articles
Copyright© 2026 Tech Science Press
© 1997-2026 TSP (Henderson, USA) unless otherwise stated

Share Link