.png){kind=icon}
返回
Feature selection using bare-bones particle swarm optimization with mutual information
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2020.107804.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Feature selection (FS) is an important data processing method in pattern recognition and data mining. Due to not considering characteristics of the FS problem itself, traditional particle update mechanisms and swarm initialization strategies adopted in most particle swarm optimization (PSO) limit their performance on dealing with high-dimensional FS problems. Focused on it, this paper proposes a novel feature selection algorithm based on bare bones PSO (BBPSO) with mutual information. Firstly, an effective swarm initialization strategy based on label correlation is developed, making full use of the correlation between features and class labels to accelerate the convergence of swarm. Then, in order to enhance the exploitation performance of the algorithm, two local search operators, i.e., the supplementary operator and the deletion operator, are developed based on feature relevance-redundancy. Furthermore, an adaptive flip mutation operator is designed to help particles jump out of local optimal solutions. We apply the proposed algorithm to typical datasets based on the K-Nearest Neighbor classifier ( K-NN), and compare it with eleven state-of-the-art algorithms, SFS, PTA , SGA , BPSO, PSO(4-2), HPSO-LS, Binary BPSO, NaFA , IBFA , KPLS-mRMR and SMBA-CSFS. The experimental results show that the proposed algorithm can achieve a feature subset with better performance, and is a highly competitive FS algorithm. (c) 2020 Elsevier Ltd. All rights reserved.
Keyword:
Feature selection
Particle swarm
Swarm initialization
Mutual information
Local search
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
暂无机构信息
引用论文
A new maximum relevance-minimum multicollinearity (MRmMC) method for feature selection and ranking.png)
PATTERN RECOGNITION
OAAI
PATTERN RECOGNITIONIF7.6
OAAI分享收藏A hybrid approach of differential evolution and artificial bee colony for feature selection一种差分进化与人工蜂群混合的特征选择方法
PREAI
分享收藏A PSO-based multi-objective multilabel feature selection method in classificationSCIENTIFIC REPORTSOAAI
SCIENTIFIC REPORTSIF3.9
OAAI分享收藏A novel feature selection method based on normalized mutual information一种基于归一化互信息的特征选择方法APPLIED INTELLIGENCE
APPLIED INTELLIGENCEIF3.5
PREAI
分享收藏Simultaneous feature selection and discretization based on mutual information基于互信息的同时特征选择和离散化PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏
查看更多内容