A new population initialization of metaheuristic algorithms based on hybrid fuzzy rough set for high-dimensional gene data feature selection

初始化 特征选择 计算机科学 元启发式 滤波器(信号处理) 人口 人工智能 数据挖掘 特征(语言学) 算法 模糊逻辑 遗传算法 粗集 维数之咒 机器学习 模式识别(心理学) 语言学 哲学 人口学 社会学 计算机视觉 程序设计语言
作者
Xuanming Guo,Jiao Hu,Helong Yu,Mingjing Wang,Bo Yang
出处
期刊:Computers in Biology and Medicine [Elsevier BV]
卷期号:166: 107538-107538 被引量:4
标识
DOI:10.1016/j.compbiomed.2023.107538
摘要

In the realm of modern medicine and biology, vast amounts of genetic data with high complexity are available. However, dealing with such high-dimensional data poses challenges due to increased processing complexity and size. Identifying critical genes to reduce data dimensionality is essential. The filter-wrapper hybrid method is a commonly used approach in feature selection. Most of these methods employ filters such as MRMR and ReliefF, but the performance of these simple filters is limited. Rough set methods, on the other hand, are a type of filter method that outperforms traditional filters. Simultaneously, many studies have pointed out the crucial importance of good initialization strategies for the performance of the metaheuristic algorithm (a type of wrapper-based method). Combining these two points, this paper proposes a novel filter-wrapper hybrid method for high-dimensional feature selection. To be specific, we utilize the variant of bWOA (binary Whale Optimization Algorithm) based on Hybrid Fuzzy Rough Set to perform attribute reduction, and the reduced attributes are used as prior knowledge to initialize the population. We then employ metaheuristics for further feature selection based on this initialized population. We conducted experiments using five different algorithms on 14 UCI datasets. The experiment results show that after applying the initialization method proposed in this article, the performance of five enhanced algorithms, has shown significant improvement. Particularly, the improved bMFO using our initialization method: fuzzy_bMFO outperformed six currently advanced algorithms, indicating that our initialization method for metaheuristic algorithms is suitable for high-dimensional feature selection tasks.
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
WL完成签到 ,获得积分10
1秒前
能吃是猪完成签到,获得积分10
3秒前
3秒前
今非完成签到,获得积分10
3秒前
4秒前
8秒前
dota1dota26完成签到,获得积分10
8秒前
8秒前
9秒前
慕青应助宫城采纳,获得10
9秒前
小马甲应助Dream Luminator采纳,获得10
10秒前
HYI发布了新的文献求助10
12秒前
笨笨紫霜完成签到,获得积分10
14秒前
量子星尘发布了新的文献求助10
14秒前
夏五鱼发布了新的文献求助10
15秒前
汉堡包应助搞怪莫茗采纳,获得10
16秒前
17秒前
fts完成签到,获得积分10
17秒前
CipherSage应助嗨Honey采纳,获得10
18秒前
小蘑菇应助笨笨紫霜采纳,获得10
19秒前
宫城发布了新的文献求助10
21秒前
22秒前
23秒前
夏五鱼完成签到 ,获得积分20
24秒前
25秒前
1.1发布了新的文献求助10
26秒前
ywy关注了科研通微信公众号
26秒前
27秒前
一一发布了新的文献求助10
28秒前
开朗熊猫发布了新的文献求助10
29秒前
29秒前
30秒前
31秒前
32秒前
32秒前
山复尔尔完成签到 ,获得积分10
32秒前
邢文瑞发布了新的文献求助10
33秒前
英姑应助唠嗑在呐采纳,获得10
33秒前
朴素代秋发布了新的文献求助10
34秒前
夕夕口口发布了新的文献求助10
36秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 700
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
Indomethacinのヒトにおける経皮吸収 400
Effective Learning and Mental Wellbeing 400
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 3976058
求助须知:如何正确求助?哪些是违规求助? 3520294
关于积分的说明 11202245
捐赠科研通 3256804
什么是DOI,文献DOI怎么找? 1798471
邀请新用户注册赠送积分活动 877610
科研通“疑难数据库(出版商)”最低求助积分说明 806496