A problem-specific non-dominated sorting genetic algorithm for supervised feature selection

DOI10.1016/J.INS.2020.08.083zbMath1475.62202OpenAlexW3082583002MaRDI QIDQ2056342

Yu Zhou, Junhao Kang, Xu Wang, Xiao Zhang, Wen-Jun Zhang

Publication date: 2 December 2021

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2020.08.083

zbMATH Keywords

classificationmulti-objective optimizationgenetic algorithmfeature selection

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Evolutionary algorithms, genetic algorithms (computational aspects) (68W50) Multi-objective and goal programming (90C29) Approximation methods and heuristics in mathematical programming (90C59)

Related Items (4)

A novel wrapper-based feature subset selection method using modified binary differential evolution algorithm ⋮ Global optimization on non-convex two-way interaction truncated linear multivariate adaptive regression splines using mixed integer quadratic programming ⋮ Streaming feature selection via graph diffusion ⋮ Unsupervised feature selection through combining graph learning and \(\ell_{2, 0}\)-norm constraint

Uses Software

• MOEA/D
• t-SNE
• Unnamed Item

Cites Work

• Unnamed Item
• Consistency-based search in feature selection
• Improved binary PSO for feature selection using gene expression data
• A discrete particle swarm optimization method for feature selection in binary classification problems
• An Introduction to Statistical Learning
• A Direct Method of Nonparametric Measurement Selection

This page was built for publication: A problem-specific non-dominated sorting genetic algorithm for supervised feature selection