{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T18:04:54Z","timestamp":1785953094548,"version":"3.56.0"},"reference-count":20,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2025,1,10]],"date-time":"2025-01-10T00:00:00Z","timestamp":1736467200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,10]],"date-time":"2025-01-10T00:00:00Z","timestamp":1736467200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2025,3]]},"DOI":"10.1007\/s00521-024-10837-4","type":"journal-article","created":{"date-parts":[[2025,1,10]],"date-time":"2025-01-10T09:36:48Z","timestamp":1736501808000},"page":"6217-6232","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Distance-based mutual congestion feature selection with genetic algorithm for high-dimensional medical datasets"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6161-0430","authenticated-orcid":false,"given":"Hossein","family":"Nematzadeh","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joseph","family":"Mani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zahra","family":"Nematzadeh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ebrahim","family":"Akbari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Radziah","family":"Mohamad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,1,10]]},"reference":[{"key":"10837_CR1","doi-asserted-by":"crossref","unstructured":"Espezua S, Villanueva E, Maciel CD, Carvalho A (2015) A Projection Pursuit framework for supervised dimension reduction of high dimensional small sample datasets. Neurocomputing 149:767\u2013776","DOI":"10.1016\/j.neucom.2014.07.057"},{"key":"10837_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2021.102228","volume":"123","author":"M Rostami","year":"2022","unstructured":"Rostami M, Forouzandeh S, Berahmand K, Soltani M, Shahsavari M, Oussalah M (2022) Gene selection for microarray data classification via multi-objective graph theoretic-based method. Artif Intell Med 123:102228","journal-title":"Artif Intell Med"},{"key":"10837_CR3","doi-asserted-by":"crossref","unstructured":"Azadifar S, Rostami M, Berahmand K, Moradi P, Oussalah M (2022) Graph-based relevancy-redundancy gene selection method for cancer diagnosis. Comput Biol Med 147:105766","DOI":"10.1016\/j.compbiomed.2022.105766"},{"key":"10837_CR4","doi-asserted-by":"crossref","unstructured":"Awadallah MA, Braik MS, Al-Betar MA, Abu Doush I (2023) An enhanced binary artificial rabbits optimization for feature selection in medical diagnosis. Neural Comput Appl 35(27):20013\u201320068","DOI":"10.1007\/s00521-023-08812-6"},{"issue":"17","key":"10837_CR5","doi-asserted-by":"publisher","first-page":"18754","DOI":"10.1007\/s11227-022-04606-0","volume":"78","author":"Y Li","year":"2022","unstructured":"Li Y, Cui X, Fan J, Wang T (2022) Global chaotic bat algorithm for feature selection. J Supercomput 78(17):18754\u201318776","journal-title":"J Supercomput"},{"key":"10837_CR6","doi-asserted-by":"publisher","unstructured":"Ahmad Zamri N, Ab Aziz NA, Bhuvaneswari T, Abdul Aziz NH, Ghazali AK (2023) Feature selection of microarray data using simulated kalman filter with mutation. Processes 11. https:\/\/doi.org\/10.3390\/pr11082409","DOI":"10.3390\/pr11082409"},{"key":"10837_CR7","doi-asserted-by":"publisher","first-page":"300","DOI":"10.1016\/j.procs.2018.01.126","volume":"127","author":"SH Bouazza","year":"2018","unstructured":"Bouazza SH, Auhmani Kh, Zeroual A, Hamdi N (2018) Selecting significant marker genes from microarray data by filter approach for cancer diagnosis. Proc Comput Sci 127:300\u2013309","journal-title":"Proc Comput Sci"},{"issue":"6","key":"10837_CR8","doi-asserted-by":"publisher","first-page":"1946","DOI":"10.1016\/j.ygeno.2019.01.006","volume":"111","author":"H Nematzadeh","year":"2019","unstructured":"Nematzadeh H, Enayatifar R, Mahmud M, Akbari E (2019) Frequency based feature selection method using whale algorithm. Genomics 111(6):1946\u20131955","journal-title":"Genomics"},{"key":"10837_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.is.2021.101760","volume":"100","author":"S Abasabadi","year":"2021","unstructured":"Abasabadi S, Nematzadeh H, Motameni H, Akbari E (2021) Automatic ensemble feature selection using fast non-dominated sorting. Inf Syst 100:101760","journal-title":"Inf Syst"},{"issue":"18","key":"10837_CR10","doi-asserted-by":"publisher","first-page":"19725","DOI":"10.1007\/s11227-022-04650-w","volume":"78","author":"S Abasabadi","year":"2022","unstructured":"Abasabadi S, Nematzadeh H, Motameni H, Akbari E (2022) Hybrid feature selection based on SLI and genetic algorithm for microarray datasets. J Supercomput 78(18):19725\u201319753","journal-title":"J Supercomput"},{"key":"10837_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2022.109699","volume":"130","author":"H Nematzadeh","year":"2022","unstructured":"Nematzadeh H, Garc\u00eda-Nieto J, Navas-Delgado I, Aldana-Montes JF (2022) Automatic frequency-based feature selection using discrete weighted evolution strategy. Appl Soft Comput 130:109699","journal-title":"Appl Soft Comput"},{"key":"10837_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.123521","volume":"249","author":"H Nematzadeh","year":"2024","unstructured":"Nematzadeh H, Garc\u00eda-Nieto J, Navas-Delgado I, Aldana-Montes JF (2024) Pattern recognition frequency-based feature selection with multi-objective discrete evolution strategy for high-dimensional medical datasets. Expert Syst Appl 249:123521","journal-title":"Expert Syst Appl"},{"key":"10837_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.114737","volume":"175","author":"B Nouri-Moghaddam","year":"2021","unstructured":"Nouri-Moghaddam B, Ghazanfari M, Fathian M (2021) A novel multi-objective forest optimization algorithm for wrapper feature selection. Expert Syst Appl 175:114737","journal-title":"Expert Syst Appl"},{"key":"10837_CR14","doi-asserted-by":"crossref","unstructured":"Prajapati S, Das H, Gourisaria MK (2023) Feature selection using genetic algorithm for microarray data classification. In: 2022 OPJU international technology conference on emerging technologies for sustainable development (OTCON)","DOI":"10.1109\/OTCON56053.2023.10113937"},{"key":"10837_CR15","doi-asserted-by":"crossref","unstructured":"Hashemi A, Dowlatshahi MB, Nezamabadi-pour H (2021) A pareto-based ensemble of feature selection algorithms. Exp Syst Appl 180:115130","DOI":"10.1016\/j.eswa.2021.115130"},{"key":"10837_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2020.104079","volume":"97","author":"Z Sadeghian","year":"2021","unstructured":"Sadeghian Z, Akbari E, Nematzadeh H (2021) A hybrid feature selection method based on information theory and binary butterfly optimization algorithm. Eng Appl Artif Intell 97:104079","journal-title":"Eng Appl Artif Intell"},{"key":"10837_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.110031","volume":"135","author":"H Pan","year":"2023","unstructured":"Pan H, Chen S, Xiong H (2023) A high-dimensional feature selection method based on modified Gray Wolf Optimization. Appl Soft Comput 135:110031","journal-title":"Appl Soft Comput"},{"key":"10837_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2022.104894","volume":"113","author":"MA Ganjei","year":"2022","unstructured":"Ganjei MA, Boostani R (2022) A hybrid feature selection scheme for high-dimensional data. Eng Appl Artif Intell 113:104894","journal-title":"Eng Appl Artif Intell"},{"issue":"2","key":"10837_CR19","first-page":"562","volume":"11","author":"W Ali","year":"2023","unstructured":"Ali W, Saeed F (2023) Hybrid Filter and Genetic Algorithm-Based Feature Selection for Improving Cancer Classification in High-Dimensional Microarray Data 11(2):562","journal-title":"Hybrid Filter and Genetic Algorithm-Based Feature Selection for Improving Cancer Classification in High-Dimensional Microarray Data"},{"key":"10837_CR20","first-page":"3165","volume":"29","author":"\u00d6S S\u00f6nmez","year":"2021","unstructured":"S\u00f6nmez \u00d6S, Da\u011ftekin \u00d6S, Ensari T (2021) Gene expression data classification using genetic algorithm-based feature selection 29:3165\u20133179","journal-title":"Gene expression data classification using genetic algorithm-based feature selection"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10837-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-10837-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10837-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,6]],"date-time":"2025-03-06T13:57:35Z","timestamp":1741269455000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-10837-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,10]]},"references-count":20,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2025,3]]}},"alternative-id":["10837"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-10837-4","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,10]]},"assertion":[{"value":"20 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 November 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 January 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors confirm that they have NO connections with or engagement with any organization or institution with any financial interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}