.png){kind=icon}
返回
Memory-based Harris hawk optimization with learning agents: a feature selection approach
.png){kind=icon}
PRE
AI
DOI：10.1007/s00366-021-01479-4.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 a vital pre-processing phase for most machine learning and data mining courses. This article proposes new variants of the Harris hawk optimization called memory energetic Harris hawk optimization (MEHHO1 and MEHHO2) to select the optimal features for classification purposes. The MEHHO approaches adopt an energetic learning strategy and memory saving and updating mechanism. The former extends the chance of the algorithm escaping the local solutions, while the latter boosts the exploitation behavior. The proposed approaches are applied in the feature selection domain for assessing a subset of high discriminative features. The proposed approaches are evaluated on 13 low-dimensional and eight high-dimensional datasets. Also, the proposed approaches are utilized to solve the feature selection problem for the classification of electromyography signals. Our results prove the capability of the proposed approaches to find the optimal feature subset compared to the other five well-known optimization algorithms. Thus, the proposed MEHHO is expected to be a promising and effective technology to solve the feature selection problem.
Keyword:
Harris hawk optimization
Feature selection
Data mining
Classification
Optimization
Electromyography
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:
4.9
论文数:
2.6K
被引数:
9.3K
机构
引用论文
Effectiveness and long-term retention of anti-tumour necrosis factor treatment in juvenile and adult patients with juvenile idiopathic arthritis: data from Reuma.pt.png)
Rheumatology
OAAI
RheumatologyIF0
OAAI分享收藏Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm蛾焰优化算法: 一种新的自然启发启发式范式
PREAI
分享收藏分享收藏Binary dragonfly optimization for feature selection using time-varying transfer functions使用时变传递函数进行特征选择的二进制蜻蜓优化OAAI
OAAI分享收藏Evolutionary competitive swarm exploring optimal support vector machines and feature weightingSOFT COMPUTING
SOFT COMPUTINGIF2.5
PREAI
分享收藏A survey on swarm intelligence approaches to feature selection in data mining数据挖掘中特征选择的群体智能方法综述
PREAI
分享收藏Multi-population differential evolution-assisted Harris hawks optimization: Framework and case studies多种群差分进化辅助Harris hawks优化: 框架与案例研究
PREAI
分享收藏
查看更多内容