A multi-objective evolutionary algorithm with interval based initialization and self-adaptive crossover operator for large-scale feature selection in classification-Reference-Cited by-同舟云学术

A multi-objective evolutionary algorithm with interval based initialization and self-adaptive crossover operator for large-scale feature selection in classification

Published:2022-09 Issue: Volume:127 Page:109420
ISSN:1568-4946
Container-title:Applied Soft Computing
language:en
Short-container-title:Applied Soft Computing

Author:

Xue Yu^ORCID,Cai Xu,Neri Ferrante

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Software

Reference62 articles.

1. A correlation guided genetic algorithm and its application to feature selection;Zhou;Appl. Soft Comput.,2022

2. Evolutionary multitasking for feature selection in high-dimensional classification via particle swarm optimization;Chen;IEEE Trans. Evol. Comput.,2021

3. Solving large-scale multiobjective optimization problems with sparse optimal solutions via unsupervised neural networks;Tian;IEEE Trans. Cybern.,2020

4. A survey on evolutionary computation approaches to feature selection;Xue;IEEE Trans. Evol. Comput.,2015

5. Feature selection: A data perspective;Li;ACM Comput. Surv.,2017

Cited by 70 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Feature selection for packer classification based on association rule mining;Engineering Applications of Artificial Intelligence;2024-11

2. Multi-class imbalance problem: A multi-objective solution;Information Sciences;2024-10

3. A space sampling based large-scale many-objective evolutionary algorithm;Information Sciences;2024-09

4. Information gain-based multi-objective evolutionary algorithm for feature selection;Information Sciences;2024-08

5. Imbalanced and missing multi-label data learning with global and local structure;Information Sciences;2024-08