Improved LLE and neighborhood rough sets-based gene selection using Lebesgue measure for cancer classification on gene expression data
Author:
Affiliation:
1. Postdoctoral Mobile Station of Biology, College of Life Science, Henan Normal University, Xinxiang, China
2. College of Computer and Information Engineering, Henan Normal University, Xinxiang, China
Publisher
IOS Press
Subject
Artificial Intelligence,General Engineering,Statistics and Probability
Reference38 articles.
1. Correlation feature selection based improved-binary particle swarm optimization for gene selection and cancer classification;Jain;Applied Soft Computing,2018
2. RPCA-based tumor classification using gene expression data;Liu;IEEE/ACM Transactions on Computational Biology and Bioinformatics,2015
3. An empirical evaluation of hierarchical feature selection methods for classification in bioinformatics datasets with gene ontology-based features;Wan;Artificial Intelligence Review,2018
4. Cancer classification using a novel gene selection approach by means of shuffling based on data clustering with optimization;Elyasigomari;Applied Soft Computing,2015
Cited by 19 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. A novel approach to discretizing information systems associated with neighborhood rough sets;International Journal of Approximate Reasoning;2024-01
2. Fuzzy-based concept-cognitive learning: An investigation of novel approach to tumor diagnosis analysis;Information Sciences;2023-08
3. The Possible Equivalent Value Set for Incomplete Data Set;Computational Science and Its Applications – ICCSA 2023 Workshops;2023
4. TSFNFR: Two-stage fuzzy neighborhood-based feature reduction with binary whale optimization algorithm for imbalanced data classification;Knowledge-Based Systems;2022-11
5. TSFNFS: two-stage-fuzzy-neighborhood feature selection with binary whale optimization algorithm;International Journal of Machine Learning and Cybernetics;2022-10-05
1.学者识别学者识别
2.学术分析学术分析
3.人才评估人才评估
"同舟云学术"是以全球学者为主线，采集、加工和组织学术论文而形成的新型学术文献查询和分析系统，可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容，当前同舟云学术共收录了国内外主流学术期刊6万余种，收集的期刊论文及会议论文总量共计约1.5亿篇，并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询！咨询电话：010-8811{复制后删除}0370
www.globalauthorid.com
TOP
Copyright © 2019-2024 北京同舟云网络信息技术有限公司 京公网安备11010802033243号 京ICP备18003416号-3