.png){kind=icon}
返回
Binary chemical reaction optimization based feature selection techniques for machine learning classification problems
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2020.114169.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 is an important pre-processing technique for dimensionality reduction of high-dimensional data in machine learning (ML) field. In this paper, we propose a binary chemical reaction optimization (BCRO) and a hybrid binary chemical reaction optimization-binary particle swarm optimization (HBCRO-BPSO) based feature selection techniques to optimize the number of selected features and improve the classification accuracy. Three objective functions have been used for the proposed feature selection techniques to compare their performances with a BPSO and advanced binary ant colony optimization (ABACO) along with an implemented GA based feature selection approach called as binary genetic algorithm (BGA). Five ML algorithms including K-nearest neighbor (KNN), logistic regression, Naive Bayes, decision tree, and random forest are considered for classification tasks. Experimental results tested on eleven benchmark datasets from UCI ML repository show that the proposed HBCRO-BPSO algorithm improves the average percentage of reduction in features (APRF) and average percentage of improvement in accuracy (APIA) by 5.01% and 3.83%, respectively over the existing BPSO based feature selection method; 4.58% and 3.12% over BGA; and 4.15% and 2.27% over ABACO when used with a KNN classifier.
Keyword:
Machine learning (ML)
Feature selection (FS)
Chemical reaction optimization (CRO)
Particle swarm optimization (PSO)
Genetic algorithm (GA)
Ant colony optimization (ACO)
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:
7.5
论文数:
2.9W
被引数:
10.2W
机构
引用论文
分享收藏分享收藏Quantitative approach to hückel rule the relations between the cycles of a molecular graph and the thermodynamic stability of a conjugated molecule.png)
Tetrahedron
TetrahedronIF0
PREAI
分享收藏
查看更多内容