{"title":"Multi-strategy fusion binary SHO guided by Pearson correlation coefficient for feature selection with cancer gene expression data","authors":"Yu-Cai Wang, Hao-Ming Song, Jie-Sheng Wang, Xin-Ru Ma, Yu-Wei Song, Yu-Liang Qi","doi":"10.1016/j.eij.2025.100639","DOIUrl":null,"url":null,"abstract":"<div><div>Cancer gene expression data is extensively utilized to address the challenges of cancer subtype diagnosis. However, this data is often characterized by high-dimensional, multi-text and multi-classification, which requires an effective feature selection (FS) method. A multi-strategy fusion binary sea-horse optimizer guided by Pearson correlation coefficient was proposed for FS with cancer gene expression data. For the multi-strategy fusion, the rest strategy is introduced in the sea-horse motor behavior stage. Subsequently, a search strategy based on symbiotic organisms of sea horses is designed for the predation stage. Finally, the elementary function dynamic weight strategy is proposed. Multi-strategy fusion enables the sea-horse optimizer (SHO) to perform dynamic exploitation and exploration in the early stage of iteration, expand the search scope initially, and narrow the search scope in the middle and later stages of the algorithm, so as to avoid the algorithm falling into the local optimal and increase the possibility of the algorithm jumping out of the local optimal, and avoid the blind search caused by elite influence. In the FS part, Pearson correlation coefficient guided strategy is proposed firstly to add or delete features. Then eight binary algorithms are derived from S-type and V-type transfer functions. The simulation experiment was divided into four parts. Firstly, the CEC-2022 test functions were used to test the performance of the multi-strategy fusion SHO, from which the best variant TanASSHO was selected, and then compared with other nine swarm intelligent optimization algorithms. Performance tests of various algorithm variants on 18 UCI datasets show that V1PTASSHO is the most effective binary version. Finally, V1PTASSHO was compared with other nine swarm intelligent optimization algorithms on 18 cancer gene expression datasets. The results demonstrate that V1PTASSHO effectively reduces feature subsets, improve classification accuracy and obtain lower fitness value. Friedman test and Wilcoxon rank sum test were used for statistical analysis to verify the effectiveness of the proposed algorithm.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"29 ","pages":"Article 100639"},"PeriodicalIF":5.0000,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Egyptian Informatics Journal","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1110866525000325","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Cancer gene expression data is extensively utilized to address the challenges of cancer subtype diagnosis. However, this data is often characterized by high-dimensional, multi-text and multi-classification, which requires an effective feature selection (FS) method. A multi-strategy fusion binary sea-horse optimizer guided by Pearson correlation coefficient was proposed for FS with cancer gene expression data. For the multi-strategy fusion, the rest strategy is introduced in the sea-horse motor behavior stage. Subsequently, a search strategy based on symbiotic organisms of sea horses is designed for the predation stage. Finally, the elementary function dynamic weight strategy is proposed. Multi-strategy fusion enables the sea-horse optimizer (SHO) to perform dynamic exploitation and exploration in the early stage of iteration, expand the search scope initially, and narrow the search scope in the middle and later stages of the algorithm, so as to avoid the algorithm falling into the local optimal and increase the possibility of the algorithm jumping out of the local optimal, and avoid the blind search caused by elite influence. In the FS part, Pearson correlation coefficient guided strategy is proposed firstly to add or delete features. Then eight binary algorithms are derived from S-type and V-type transfer functions. The simulation experiment was divided into four parts. Firstly, the CEC-2022 test functions were used to test the performance of the multi-strategy fusion SHO, from which the best variant TanASSHO was selected, and then compared with other nine swarm intelligent optimization algorithms. Performance tests of various algorithm variants on 18 UCI datasets show that V1PTASSHO is the most effective binary version. Finally, V1PTASSHO was compared with other nine swarm intelligent optimization algorithms on 18 cancer gene expression datasets. The results demonstrate that V1PTASSHO effectively reduces feature subsets, improve classification accuracy and obtain lower fitness value. Friedman test and Wilcoxon rank sum test were used for statistical analysis to verify the effectiveness of the proposed algorithm.
期刊介绍:
The Egyptian Informatics Journal is published by the Faculty of Computers and Artificial Intelligence, Cairo University. This Journal provides a forum for the state-of-the-art research and development in the fields of computing, including computer sciences, information technologies, information systems, operations research and decision support. Innovative and not-previously-published work in subjects covered by the Journal is encouraged to be submitted, whether from academic, research or commercial sources.