Copula entropy-based golden jackal optimization algorithm for high-dimensional feature selection problems

计算机科学 特征选择 算法 元启发式 人工智能 维数之咒 局部最优 水准点(测量) 机器学习 数据挖掘 大地测量学 地理
作者
Heba Askr,Mahmoud Abdel-Salam,Aboul Ella Hassanien
出处
期刊:Expert Systems With Applications [Elsevier BV]
卷期号:238: 121582-121582 被引量:38
标识
DOI:10.1016/j.eswa.2023.121582
摘要

Feature selection (FS) is a crucial process that aims to remove unnecessary features from datasets. It plays a role in data mining and machine learning (ML) by reducing the risk associated with high-dimensional datasets. FS is considered a challenging problem that is difficult to solve efficiently due to its combinatorial nature. As the size of the problem increases, the computation time also grows. Recently, researchers have focused on metaheuristic FS algorithms specifically designed for high-dimensional datasets. Therefore, this article proposes a powerful metaheuristic algorithm called Binary Enhanced Golden Jackal Optimization (BEGJO), which is an improved version of the recently published Golden Jackal Optimization (GJO) algorithm. The original GJO algorithm faces challenges when dealing with high-dimensional FS problems, as it tends to get trapped in local optima. To address this issue, various enhancement strategies are employed to improve the efficiency of GJO. The proposed BEGJO algorithm utilizes Copula Entropy (CE) to reduce the dimensionality of high-dimensional FS problems while maintaining high classification accuracy using the K-Nearest Neighbour (K-NN) classifier. Additionally, four enhancement strategies are incorporated to enhance the exploration and exploitation capabilities of the fundamental GJO algorithm. The BEGJO algorithm is transformed into its binary form using the sigmoid transfer function, aligning it with the nature of the FS problem. It is then tested on various high-dimensional benchmark datasets. The effectiveness of BEGJO is evaluated by comparing it with well-known algorithms in terms of classification accuracy, feature dimension, and processing time. BEGJO outperforms other algorithms in terms of classification accuracy and feature dimension and ranks up to fourth in terms of processing time. Furthermore, the advantageous use of CE is demonstrated by comparing the performance of the proposed algorithm with traditional FS algorithms. Statistical evaluations are conducted to further validate the effectiveness and superiority of the proposed algorithm. The results confirm that BEGJO is an effective solution for high-dimensional FS problems.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
lm发布了新的文献求助10
刚刚
无所归兮应助曲艺采纳,获得10
1秒前
1秒前
yar应助alone采纳,获得30
2秒前
za==应助小赵采纳,获得10
2秒前
3秒前
豆芽发布了新的文献求助10
3秒前
oky发布了新的文献求助10
3秒前
wdy111应助迷路硬币采纳,获得20
5秒前
5秒前
6秒前
艺高人胆大鸡腿完成签到 ,获得积分10
9秒前
乐乐应助焦糖采纳,获得10
9秒前
科研通AI2S应助nalan采纳,获得10
10秒前
静_完成签到 ,获得积分10
10秒前
10秒前
雪白元蝶发布了新的文献求助10
11秒前
11秒前
11秒前
留白完成签到 ,获得积分10
12秒前
共享精神应助小圆采纳,获得10
12秒前
12秒前
慕青应助梵高的向日葵采纳,获得10
12秒前
SYLH应助科研通管家采纳,获得20
12秒前
czh应助科研通管家采纳,获得10
12秒前
12秒前
ding应助科研通管家采纳,获得10
12秒前
搜集达人应助科研通管家采纳,获得10
13秒前
打打应助科研通管家采纳,获得10
13秒前
13秒前
彭于彦祖应助科研通管家采纳,获得10
13秒前
彭于彦祖应助科研通管家采纳,获得30
13秒前
雯雯完成签到,获得积分10
13秒前
13秒前
13秒前
研友_VZG7GZ应助科研通管家采纳,获得10
13秒前
ED应助科研通管家采纳,获得10
13秒前
共享精神应助科研通管家采纳,获得10
13秒前
JamesPei应助科研通管家采纳,获得10
14秒前
小蘑菇应助科研通管家采纳,获得10
14秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
Economic Geography and Public Policy 900
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3988786
求助须知:如何正确求助?哪些是违规求助? 3531116
关于积分的说明 11252493
捐赠科研通 3269766
什么是DOI,文献DOI怎么找? 1804771
邀请新用户注册赠送积分活动 881870
科研通“疑难数据库(出版商)”最低求助积分说明 809021