{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T17:36:02Z","timestamp":1780421762086,"version":"3.54.1"},"reference-count":95,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T00:00:00Z","timestamp":1740441600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T00:00:00Z","timestamp":1740441600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100024743","name":"Suez Canal University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100024743","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cluster Comput"],"published-print":{"date-parts":[[2025,8]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Traditional classification algorithms struggle with the high dimensionality of medical data, resulting in reduced performance in tasks like disease diagnosis. Feature selection (FS) has emerged as a crucial preprocessing step to mitigate these challenges by extracting relevant features and improving classification accuracy. This paper proposes a hybrid FS method, FJMIBCOA, which integrates Fuzzy Joint Mutual Information (FJMI) as a filter measure and Binary Cheetah Optimizer Algorithm (BCOA) as a wrapper method. Unlike existing hybrid FS methods, the proposed method employs FJMI to address uncertainty in feature relationships, providing several advantages such as handling both discrete and continuous features, accommodating linear and non-linear relationships, noise robustness and effectively utilizing intra- and inter-class information. It also employs BCOA as a wrapper method, requiring a few parameters, minimizing computational overhead and enhancing classification robustness, making it an efficient and adaptable solution for FS in complex medical datasets. The proposed method is validated on 23 medical datasets and 14 high-dimensional microarray datasets, demonstrating excellent performance in terms of fitness value, accuracy and feature size. FJMIBCOA surpasses existing methods in medical datasets by achieving higher accuracy in 78.26% of datasets while reducing the feature size by 84.79%. Similarly, in microarray datasets, it improves accuracy in 78.58% of datasets with an impressive 95.08% reduction in feature size. Furthermore, FJMIBCOA achieves superior accuracy in 60% of datasets while selecting fewer features in 78.57% of datasets as compared to previous studies. Statistical testing indicates that FJMIBCOA outperforms other methods significantly. The proposed method enhances diagnosis accuracy and minimizes medical testing requirements, making it suitable for real-world, high-dimensional datasets and decision-making in medical data analysis. The findings from gene expression analysis emphasize the biological significance of the top selected genes, providing new insights into their potential roles in disease progression.<\/jats:p>","DOI":"10.1007\/s10586-025-05102-9","type":"journal-article","created":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T13:40:48Z","timestamp":1740490848000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Optimizing medical data classification: integrating hybrid fuzzy joint mutual information with binary Cheetah optimizer algorithm"],"prefix":"10.1007","volume":"28","author":[{"given":"Ah. E.","family":"Hegazy","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"B.","family":"Hafiz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M. A.","family":"Makhlouf","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Omar A. M.","family":"Salem","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,2,25]]},"reference":[{"key":"5102_CR1","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1080\/13102818.2017.1364977","volume":"32","author":"FSG Hashemi","year":"2018","unstructured":"Hashemi, F.S.G., et al.: Intelligent mining of large-scale bio-data: bioinformatics applications. Biotechnol. Biotechnol. Equip. 32, 10\u201329 (2018)","journal-title":"Biotechnol. Biotechnol. Equip."},{"key":"5102_CR2","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/j.ins.2014.05.042","volume":"282","author":"V Bol\u00f3n-Canedo","year":"2014","unstructured":"Bol\u00f3n-Canedo, V., S\u00e1nchez-Marono, N., Alonso-Betanzos, A., Ben\u00edtez, J.M., Herrera, F.: A review of microarray datasets and applied feature selection methods. Inf. Sci. 282, 111\u2013135 (2014)","journal-title":"Inf. Sci."},{"key":"5102_CR3","doi-asserted-by":"publisher","first-page":"531","DOI":"10.1126\/science.286.5439.531","volume":"286","author":"TR Golub","year":"1999","unstructured":"Golub, T.R., et al.: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286, 531\u2013537 (1999)","journal-title":"Science"},{"key":"5102_CR4","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1016\/j.neucom.2015.01.070","volume":"159","author":"G Chen","year":"2015","unstructured":"Chen, G., Chen, J.: A novel wrapper method for feature selection and its applications. Neurocomputing 159, 219\u2013226 (2015)","journal-title":"Neurocomputing"},{"key":"5102_CR5","doi-asserted-by":"publisher","first-page":"4632","DOI":"10.1016\/j.eswa.2015.01.069","volume":"42","author":"N Dess\u00ec","year":"2015","unstructured":"Dess\u00ec, N., Pes, B.: Similarity of feature selection methods: an empirical study across data intensive classification tasks. Expert Syst. Appl. 42, 4632\u20134642 (2015)","journal-title":"Expert Syst. Appl."},{"key":"5102_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-022-14338-z","volume":"12","author":"MA Akbari","year":"2022","unstructured":"Akbari, M.A., Zare, M., Azizipanah-abarghooee, R., Mirjalili, S., Deriche, M.: The cheetah optimizer: a nature-inspired metaheuristic algorithm for large-scale optimization problems. Sci. Rep. 12, 1\u201320 (2022)","journal-title":"Sci. Rep."},{"key":"5102_CR7","doi-asserted-by":"crossref","unstructured":"Eberhart, R., Kennedy, J.: New optimizer using particle swarm theory. In: Proceedings of the International Symposium on Micro Machine and Human Science, pp. 39\u201343 (1995)","DOI":"10.1109\/MHS.1995.494215"},{"key":"5102_CR8","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/j.knosys.2018.08.003","volume":"161","author":"M Mafarja","year":"2018","unstructured":"Mafarja, M., et al.: Binary dragonfly optimization for feature selection using time-varying transfer functions. Knowl.-Based Syst. 161, 185\u2013204 (2018)","journal-title":"Knowl.-Based Syst."},{"key":"5102_CR9","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1007\/s11517-018-1874-4","volume":"57","author":"M Ghosh","year":"2019","unstructured":"Ghosh, M., et al.: Genetic algorithm based cancerous gene identification from microarray data using ensemble of filter methods. Med. Biol. Eng. Comput. 57, 159\u2013176 (2019)","journal-title":"Med. Biol. Eng. Comput."},{"key":"5102_CR10","first-page":"1205","volume":"5","author":"Yu Lei","year":"2004","unstructured":"Lei, Yu., Huan, Liu: Efficient feature selection via analysis of relevance and redundancy. J. Mach. Learn. Res. 5, 1205\u20131224 (2004)","journal-title":"J. Mach. Learn. Res."},{"key":"5102_CR11","doi-asserted-by":"crossref","unstructured":"Gharroudi, O., Elghazel, H., Aussem, A.: A comparison of multi-label feature selection methods using the random forest paradigm. In: Advances in Artificial Intelligence: 27th Canadian Conference on Artificial Intelligence, Canadian AI, Montr\u00e9al, QC, Canada, May 6\u20139, 2014. Proceedings, vol. 27, pp. 95\u2013106 (2014)","DOI":"10.1007\/978-3-319-06483-3_9"},{"key":"5102_CR12","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1109\/4235.585893","volume":"1","author":"DH Wolpert","year":"1997","unstructured":"Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1, 67\u201382 (1997)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"5102_CR13","first-page":"85","volume":"2","author":"VK Yarlagadda","year":"2021","unstructured":"Yarlagadda, V.K.: Harnessing biomedical signals: A modern fusion of hadoop infrastructure, ai, and fuzzy logic in healthcare. Malays. J. Med. Biol. Res. 2, 85\u201392 (2021)","journal-title":"Malays. J. Med. Biol. Res."},{"key":"5102_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.124396","volume":"254","author":"K Thirumoorthy","year":"2024","unstructured":"Thirumoorthy, K., Britto, J.J.: A two-stage feature selection approach using hybrid elitist self-adaptive cat and mouse based optimization algorithm for document classification. Expert Syst. Appl. 254, 124396 (2024)","journal-title":"Expert Syst. Appl."},{"key":"5102_CR15","doi-asserted-by":"publisher","first-page":"305","DOI":"10.3934\/mbe.2021016","volume":"18","author":"OAM Salem","year":"2020","unstructured":"Salem, O.A.M., et al.: Feature selection based on fuzzy joint mutual information maximization. Math. Biosci. Eng. 18, 305\u2013327 (2020)","journal-title":"Math. Biosci. Eng."},{"key":"5102_CR16","doi-asserted-by":"publisher","first-page":"1842","DOI":"10.3390\/electronics13101842","volume":"13","author":"S Chen","year":"2024","unstructured":"Chen, S., Ji, Y., Sun, X.: Multi-user detection based on improved cheetah optimization algorithm. Electronics 13, 1842 (2024)","journal-title":"Electronics"},{"key":"5102_CR17","doi-asserted-by":"publisher","first-page":"577","DOI":"10.4314\/gjpas.v30i4.15","volume":"30","author":"AO Otobi","year":"2024","unstructured":"Otobi, A.O., et al.: The computational effect and hyperparameters tuning of deep convolutional layer depth of high-ranking tuberculosis detection models. Glob. J. Pure Appl. Sci. 30, 577\u2013584 (2024)","journal-title":"Glob. J. Pure Appl. Sci."},{"key":"5102_CR18","doi-asserted-by":"publisher","first-page":"3801","DOI":"10.1007\/s13369-018-3680-6","volume":"44","author":"AE Hegazy","year":"2019","unstructured":"Hegazy, A.E., Makhlouf, M., El-Tawel, G.S.: Feature selection using chaotic salp swarm algorithm for data classification. Arab. J. Sci. Eng. 44, 3801\u20133816 (2019)","journal-title":"Arab. J. Sci. Eng."},{"key":"5102_CR19","doi-asserted-by":"publisher","first-page":"11553","DOI":"10.1007\/s10586-024-04557-6","volume":"27","author":"H Quan","year":"2024","unstructured":"Quan, H., Zhang, Y., Li, Q., Liu, Y.: Tpbfs: two populations based feature selection method for medical data. Cluster Comput. 27, 11553\u201311568 (2024)","journal-title":"Cluster Comput."},{"key":"5102_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.112898","volume":"140","author":"H Faris","year":"2020","unstructured":"Faris, H., et al.: Time-varying hierarchical chains of salps with random weight networks for feature selection. Expert Syst. Appl. 140, 112898 (2020)","journal-title":"Expert Syst. Appl."},{"key":"5102_CR21","doi-asserted-by":"publisher","first-page":"39496","DOI":"10.1109\/ACCESS.2019.2906757","volume":"7","author":"Q Al-Tashi","year":"2019","unstructured":"Al-Tashi, Q., Kadir, S.J.A., Rais, H.M., Mirjalili, S., Alhussian, H.: Binary optimization using hybrid grey wolf optimization for feature selection. IEEE Access 7, 39496\u201339508 (2019)","journal-title":"IEEE Access"},{"key":"5102_CR22","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1016\/j.jksuci.2018.06.003","volume":"32","author":"AE Hegazy","year":"2020","unstructured":"Hegazy, A.E., Makhlouf, M.A., El-Tawel, G.S.: Improved salp swarm algorithm for feature selection. J. King Saud Univer.\u2014Comput. Inf. Sci. 32, 335\u2013344 (2020)","journal-title":"J. King Saud Univer.\u2014Comput. Inf. Sci."},{"key":"5102_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2021.104210","volume":"100","author":"M Rostami","year":"2021","unstructured":"Rostami, M., Berahmand, K., Nasiri, E., Forouzande, S.: Review of swarm intelligence-based feature selection methods. Eng. Appl. Artif. Intell. 100, 104210 (2021)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"5102_CR24","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1016\/j.neucom.2022.04.083","volume":"494","author":"T Dokeroglu","year":"2022","unstructured":"Dokeroglu, T., Deniz, A., Kiziloz, H.E.: A comprehensive survey on recent metaheuristics for feature selection. Neurocomputing 494, 269\u2013296 (2022)","journal-title":"Neurocomputing"},{"key":"5102_CR25","doi-asserted-by":"publisher","first-page":"26766","DOI":"10.1109\/ACCESS.2021.3056407","volume":"9","author":"P Agrawal","year":"2021","unstructured":"Agrawal, P., Abutarboush, H.F., Ganesh, T., Mohamed, A.W.: Metaheuristic algorithms on feature selection: a survey of one decade of research (2009\u20132019). IEEE Access 9, 26766\u201326791 (2021)","journal-title":"IEEE Access"},{"key":"5102_CR26","doi-asserted-by":"publisher","first-page":"42617","DOI":"10.1007\/s11042-023-15143-0","volume":"82","author":"S Asghari","year":"2023","unstructured":"Asghari, S., Nematzadeh, H., Akbari, E., Motameni, H.: Mutual information-based filter hybrid feature selection method for medical datasets using feature clustering. Multimedia Tools Appl. 82, 42617\u201342639 (2023)","journal-title":"Multimedia Tools Appl."},{"key":"5102_CR27","doi-asserted-by":"publisher","first-page":"855","DOI":"10.3233\/JAD-170069","volume":"65","author":"Y Zhang","year":"2018","unstructured":"Zhang, Y., et al.: Multivariate approach for Alzheimer\u2019s disease detection using stationary wavelet entropy and predator-prey particle swarm optimization. J. Alzheimers Dis. 65, 855\u2013869 (2018)","journal-title":"J. Alzheimers Dis."},{"key":"5102_CR28","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1007\/s12293-018-0269-2","volume":"11","author":"M Hammami","year":"2019","unstructured":"Hammami, M., Bechikh, S., Hung, C.C., Said, L.B.: A multi-objective hybrid filter-wrapper evolutionary approach for feature selection. Memet. Comput. 11, 193\u2013208 (2019)","journal-title":"Memet. Comput."},{"key":"5102_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115312","volume":"183","author":"A Got","year":"2021","unstructured":"Got, A., Moussaoui, A., Zouache, D.: Hybrid filter-wrapper feature selection using whale optimization algorithm: A multi-objective approach. Expert Syst. Appl. 183, 115312 (2021)","journal-title":"Expert Syst. Appl."},{"key":"5102_CR30","doi-asserted-by":"publisher","first-page":"4625","DOI":"10.1016\/j.ins.2010.05.037","volume":"181","author":"A Unler","year":"2011","unstructured":"Unler, A., Murat, A., Chinnam, R.B.: mr2pso: a maximum relevance minimum redundancy feature selection method based on swarm intelligence for support vector machine classification. Inf. Sci. 181, 4625\u20134641 (2011)","journal-title":"Inf. Sci."},{"key":"5102_CR31","doi-asserted-by":"publisher","first-page":"13005","DOI":"10.1007\/s10586-024-04614-0","volume":"27","author":"X Qin","year":"2024","unstructured":"Qin, X., Zhang, S., Dong, X., Shi, H., Yuan, L.: Improved aquila optimizer with MRMR for feature selection of high-dimensional gene expression data. Clust. Comput. 27, 13005\u201313027 (2024)","journal-title":"Clust. Comput."},{"key":"5102_CR32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-018-37186-2","volume":"9","author":"L Sun","year":"2019","unstructured":"Sun, L., et al.: A hybrid gene selection method based on relieff and ant colony optimization algorithm for tumor classification. Sci. Rep. 9, 1\u201314 (2019)","journal-title":"Sci. Rep."},{"key":"5102_CR33","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1007\/s12539-020-00372-w","volume":"12","author":"G Zhang","year":"2020","unstructured":"Zhang, G., Hou, J., Wang, J., Yan, C., Luo, J.: Feature selection for microarray data classification using hybrid information gain and a modified binary krill herd algorithm. Interdiscipl. Sci.\u2014Comput. Life Sci. 12, 288\u2013301 (2020)","journal-title":"Interdiscipl. Sci.\u2014Comput. Life Sci."},{"key":"5102_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.114012","volume":"166","author":"A Dabba","year":"2021","unstructured":"Dabba, A., Tari, A., Meftali, S., Mokhtari, R.: Gene selection and classification of microarray data method based on mutual information and moth flame algorithm. Expert Syst. Appl. 166, 114012 (2021)","journal-title":"Expert Syst. Appl."},{"key":"5102_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.chemolab.2022.104618","volume":"228","author":"ZA Varzaneh","year":"2022","unstructured":"Varzaneh, Z.A., Hossein, S., Mood, S.E., Javidi, M.M.: A new hybrid feature selection based on improved equilibrium optimization. Chemom. Intell. Lab. Syst. 228, 104618 (2022)","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"5102_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.119612","volume":"218","author":"AM Vommi","year":"2023","unstructured":"Vommi, A.M., Battula, T.K.: A hybrid filter-wrapper feature selection using fuzzy KNN based on bonferroni mean for medical datasets classification: A covid-19 case study. Expert Syst. Appl. 218, 119612 (2023)","journal-title":"Expert Syst. Appl."},{"key":"5102_CR37","doi-asserted-by":"publisher","first-page":"562","DOI":"10.3390\/pr11020562","volume":"11","author":"W Ali","year":"2023","unstructured":"Ali, W., Saeed, F.: Hybrid filter and genetic algorithm-based feature selection for improving cancer classification in high-dimensional microarray data. Processes 11, 562 (2023)","journal-title":"Processes"},{"key":"5102_CR38","doi-asserted-by":"publisher","first-page":"12299","DOI":"10.1007\/s00521-024-09713-y","volume":"36","author":"H Singh","year":"2024","unstructured":"Singh, H., Kaur, M., Singh, B.: A hybrid feature weighting and selection-based strategy to classify the high-dimensional and imbalanced medical data. Neural Comput. Appl. 36, 12299\u201312316 (2024)","journal-title":"Neural Comput. Appl"},{"key":"5102_CR39","doi-asserted-by":"crossref","unstructured":"Nematzadeh, H., Mani, J., Nematzadeh, Z., Akbari, E., Mohamad, R.: Distance-based mutual congestion feature selection with genetic algorithm for high-dimensional medical datasets. arXiv preprint arXiv:2407.15611 (2024)","DOI":"10.1007\/s00521-024-10837-4"},{"key":"5102_CR40","doi-asserted-by":"publisher","first-page":"21319","DOI":"10.1007\/s11042-023-16353-2","volume":"83","author":"S Sucharita","year":"2024","unstructured":"Sucharita, S., Sahu, B., Swarnkar, T., Meher, S.K.: Classification of cancer microarray data using a two-step feature selection framework with moth-flame optimization and extreme learning machine. Multimedia Tools Appl. 83, 21319\u201321346 (2024)","journal-title":"Multimedia Tools Appl."},{"key":"5102_CR41","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1007\/s00521-013-1368-0","volume":"24","author":"JR Vergara","year":"2014","unstructured":"Vergara, J.R., Est\u00e9vez, P.A.: A review of feature selection methods based on mutual information. Neural Comput. Appl. 24, 175\u2013186 (2014)","journal-title":"Neural Comput. Appl."},{"key":"5102_CR42","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.fss.2018.07.006","volume":"360","author":"C Wang","year":"2019","unstructured":"Wang, C., Huang, Y., Shao, M., Chen, D.: Uncertainty measures for general fuzzy relations. Fuzzy Sets Syst. 360, 82\u201396 (2019)","journal-title":"Fuzzy Sets Syst."},{"key":"5102_CR43","first-page":"2338","volume":"27","author":"Z Li","year":"2019","unstructured":"Li, Z., et al.: Uncertainty measurement for a fuzzy relation information system. IEEE Trans. Fuzzy Syst. 27, 2338\u20132352 (2019)","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"5102_CR44","doi-asserted-by":"publisher","first-page":"3509","DOI":"10.1016\/j.patcog.2007.03.017","volume":"40","author":"Q Hu","year":"2007","unstructured":"Hu, Q., Xie, Z., Yu, D.: Hybrid attribute reduction based on a novel fuzzy-rough model and information granulation. Pattern Recogn. 40, 3509\u20133521 (2007)","journal-title":"Pattern Recogn."},{"key":"5102_CR45","first-page":"619","volume":"4","author":"D Yu","year":"2011","unstructured":"Yu, D., An, S., Hu, Q.: Fuzzy mutual information based min-redundancy and max-relevance heterogeneous feature selection. Int. J. Comput. Intell. Syst. 4, 619\u2013633 (2011)","journal-title":"Int. J. Comput. Intell. Syst."},{"key":"5102_CR46","doi-asserted-by":"publisher","first-page":"414","DOI":"10.1016\/j.patrec.2005.09.004","volume":"27","author":"Q Hu","year":"2006","unstructured":"Hu, Q., Yu, D., Xie, Z.: Information-preserving hybrid data reduction based on fuzzy-rough techniques. Pattern Recogn. Lett. 27, 414\u2013423 (2006)","journal-title":"Pattern Recogn. Lett."},{"key":"5102_CR47","doi-asserted-by":"publisher","first-page":"151525","DOI":"10.1109\/ACCESS.2019.2948095","volume":"7","author":"X Wang","year":"2019","unstructured":"Wang, X., Guo, B., Shen, Y., Zhou, C., Duan, X.: Input feature selection method based on feature set equivalence and mutual information gain maximization. IEEE Access 7, 151525\u2013151538 (2019)","journal-title":"IEEE Access"},{"key":"5102_CR48","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.ijar.2021.01.003","volume":"132","author":"OA Salem","year":"2021","unstructured":"Salem, O.A., Liu, F., Chen, Y.-P.P., Chen, X.: Feature selection and threshold method based on fuzzy joint mutual information. Int. J. Approx. Reason. 132, 107\u2013126 (2021)","journal-title":"Int. J. Approx. Reason."},{"key":"5102_CR49","doi-asserted-by":"publisher","first-page":"11073","DOI":"10.1007\/s10489-022-03365-y","volume":"53","author":"H Zhong","year":"2023","unstructured":"Zhong, H., Zhang, P., Liu, G.: Multi-label feature selection via redundancy of the selected feature set. Appl. Intell. 53, 11073\u201311091 (2023)","journal-title":"Appl. Intell."},{"key":"5102_CR50","doi-asserted-by":"publisher","first-page":"1230","DOI":"10.1515\/mt-2023-0015","volume":"65","author":"SM Sait","year":"2023","unstructured":"Sait, S.M., Mehta, P., G\u00fcrses, D., Yildiz, A.R.: Cheetah optimization algorithm for optimum design of heat exchangers. Mater. Test. 65, 1230\u20131236 (2023)","journal-title":"Mater. Test."},{"key":"5102_CR51","doi-asserted-by":"publisher","first-page":"9997","DOI":"10.3390\/app13189997","volume":"13","author":"M Tostado-V\u00e9liz","year":"2023","unstructured":"Tostado-V\u00e9liz, M., et al.: An improved cheetah optimizer for accurate and reliable estimation of unknown parameters in photovoltaic cell and module models. Appl. Sci. 13, 9997 (2023)","journal-title":"Appl. Sci."},{"key":"5102_CR52","first-page":"103","volume":"16","author":"H Ghaedi","year":"2022","unstructured":"Ghaedi, H., Tabbakh, S.R.K., Ghaemi, R.: Improving performance of the convolutional neural networks for electricity theft detection by using cheetah optimization algorithm. Majlesi J. Electr. Eng. 16, 103\u2013115 (2022)","journal-title":"Majlesi J. Electr. Eng."},{"key":"5102_CR53","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1007\/s12559-019-09668-6","volume":"12","author":"M Mafarja","year":"2020","unstructured":"Mafarja, M., et al.: Efficient hybrid nature-inspired binary optimizers for feature selection. Cogn. Comput. 12, 150\u2013175 (2020)","journal-title":"Cogn. Comput."},{"key":"5102_CR54","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1016\/j.neucom.2015.06.083","volume":"172","author":"E Emary","year":"2016","unstructured":"Emary, E., Zawbaa, H.M., Hassanien, A.E.: Binary grey wolf optimization approaches for feature selection. Neurocomputing 172, 371\u2013381 (2016)","journal-title":"Neurocomputing"},{"key":"5102_CR55","unstructured":"Uci machine learning repository. http:\/\/archive.ics.uci.edu\/ml\/index.php"},{"key":"5102_CR56","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-21606-5","volume-title":"The Elements of Statistical Learning","author":"T Hastie","year":"2001","unstructured":"Hastie, T., Friedman, J., Tibshirani, R.: The Elements of Statistical Learning, vol. 1. Springer, Berlin (2001)"},{"key":"5102_CR57","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107346","volume":"106","author":"Z Beheshti","year":"2021","unstructured":"Beheshti, Z.: Utf: upgrade transfer function for binary meta-heuristic algorithms. Appl. Soft Comput. 106, 107346 (2021)","journal-title":"Appl. Soft Comput."},{"key":"5102_CR58","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/j.neucom.2016.03.101","volume":"213","author":"E Emary","year":"2016","unstructured":"Emary, E., Zawbaa, H.M., Hassanien, A.E.: Binary ant lion approaches for feature selection. Neurocomputing 213, 54\u201365 (2016)","journal-title":"Neurocomputing"},{"key":"5102_CR59","doi-asserted-by":"crossref","unstructured":"Bol\u00f3n-Canedo, V., S\u00e1nchez-Maro\u00f1o, N., Alonso-Betanzos, A.: Feature Selection for High-dimensional Data, vol. 1. Springer (2015)","DOI":"10.1007\/978-3-319-21858-8_1"},{"key":"5102_CR60","doi-asserted-by":"publisher","DOI":"10.1016\/j.xinn.2024.100722","volume":"5","author":"G Yu","year":"2024","unstructured":"Yu, G.: Thirteen years of clusterprofiler. The Innovation 5, 100722 (2024)","journal-title":"The Innovation"},{"key":"5102_CR61","doi-asserted-by":"publisher","first-page":"693","DOI":"10.1007\/s00500-007-0251-2","volume":"12","author":"S Li","year":"2008","unstructured":"Li, S., Wu, X., Hu, X.: Gene selection using genetic algorithm and support vectors machines. Soft. Comput. 12, 693\u2013698 (2008)","journal-title":"Soft. Comput."},{"key":"5102_CR62","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2023.e17101","volume":"9","author":"M Guo","year":"2023","unstructured":"Guo, M., Li, X., Li, J., Li, B.: Identification of the prognostic biomarkers and their correlations with immune infiltration in colorectal cancer through bioinformatics analysis and in vitro experiments. Heliyon 9, e17101 (2023)","journal-title":"Heliyon"},{"key":"5102_CR63","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1080\/21655979.2021.2008641","volume":"13","author":"M Feng","year":"2022","unstructured":"Feng, M., Dong, N., Zhou, X., Ma, L., Xiang, R.: Myosin light chain 9 promotes the proliferation, invasion, migration and angiogenesis of colorectal cancer cells by binding to yes-associated protein 1 and regulating hippo signaling. Bioengineered 13, 96\u2013106 (2022)","journal-title":"Bioengineered"},{"key":"5102_CR64","doi-asserted-by":"publisher","first-page":"1398268","DOI":"10.1155\/2022\/1398268","volume":"2022","author":"H-P Zhang","year":"2022","unstructured":"Zhang, H.-P., Wu, J., Liu, Z.-F., Gao, J.-W., Li, S.-Y.: Sparcl1 is a novel prognostic biomarker and correlates with tumor microenvironment in colorectal cancer. Biomed. Res. Int. 2022, 1398268 (2022)","journal-title":"Biomed. Res. Int."},{"key":"5102_CR65","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2022.845931","volume":"12","author":"Y Li","year":"2022","unstructured":"Li, Y., et al.: The hnrnpk\/a1\/r\/u complex regulates gene transcription and translation and is a favorable prognostic biomarker for human colorectal adenocarcinoma. Front. Oncol. 12, 845931 (2022)","journal-title":"Front. Oncol."},{"key":"5102_CR66","doi-asserted-by":"publisher","first-page":"2125","DOI":"10.3390\/cancers13092125","volume":"13","author":"MC Liebl","year":"2021","unstructured":"Liebl, M.C., Hofmann, T.G.: The role of p53 signaling in colorectal cancer. Cancers 13, 2125 (2021)","journal-title":"Cancers"},{"key":"5102_CR67","doi-asserted-by":"publisher","DOI":"10.1016\/j.biopha.2023.116040","volume":"170","author":"M Yue","year":"2024","unstructured":"Yue, M., et al.: The functional roles of chemokines and chemokine receptors in colorectal cancer progression. Biomed. Pharmacother. 170, 116040 (2024)","journal-title":"Biomed. Pharmacother."},{"key":"5102_CR68","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1038\/s41420-024-01990-9","volume":"10","author":"Y Ma","year":"2024","unstructured":"Ma, Y., et al.: Cebpb-mediated upregulation of serpina1 promotes colorectal cancer progression by enhancing stat3 signaling. Cell Death Discov. 10, 219 (2024)","journal-title":"Cell Death Discov."},{"key":"5102_CR69","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3892\/ol.2021.12998","volume":"22","author":"Z Zhao","year":"2021","unstructured":"Zhao, Z., Lu, L., Li, W.: Tagln2 promotes the proliferation, invasion, migration and epithelial-mesenchymal transition of colorectal cancer cells by activating stat3 signaling through anxa2. Oncol. Lett. 22, 1\u201311 (2021)","journal-title":"Oncol. Lett."},{"key":"5102_CR70","doi-asserted-by":"publisher","first-page":"1441","DOI":"10.21037\/atm-21-4192","volume":"9","author":"C Li","year":"2021","unstructured":"Li, C. et al.: Cald1 promotes the expression of pd-l1 in bladder cancer via the jak\/stat signaling pathway. Ann. Transl. Med. 9, 1441 (2021)","journal-title":"Ann. Transl. Med."},{"key":"5102_CR71","doi-asserted-by":"publisher","first-page":"207","DOI":"10.7150\/ijms.88039","volume":"21","author":"J Wang","year":"2024","unstructured":"Wang, J., et al.: The prognostic and therapeutic roles of arl-6 gene in hepatocellular carcinoma. Int. J. Med. Sci. 21, 207 (2024)","journal-title":"Int. J. Med. Sci."},{"key":"5102_CR72","doi-asserted-by":"publisher","first-page":"13295","DOI":"10.3390\/ijms241713295","volume":"24","author":"Y Meng","year":"2023","unstructured":"Meng, Y., et al.: Research advances in the role of the tropomyosin family in cancer. Int. J. Mol. Sci. 24, 13295 (2023)","journal-title":"Int. J. Mol. Sci."},{"key":"5102_CR73","doi-asserted-by":"publisher","first-page":"742","DOI":"10.1002\/glia.24308","volume":"71","author":"SW Doutt","year":"2023","unstructured":"Doutt, S.W., Longo, J.F., Carroll, S.L.: Lpar1 and aberrantly expressed lpar3 differentially promote the migration and proliferation of malignant peripheral nerve sheath tumor cells. Glia 71, 742\u2013757 (2023)","journal-title":"Glia"},{"key":"5102_CR74","first-page":"6499","volume":"11","author":"G Yang","year":"2019","unstructured":"Yang, G. et al.: Circ-itga7 sponges mir-3187-3p to upregulate asxl1, suppressing colorectal cancer proliferation. Cancer Manag Res. 11, 6499\u20136509 (2019)","journal-title":"Glia"},{"key":"5102_CR75","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1186\/s12943-021-01441-4","volume":"20","author":"G Zhu","year":"2021","unstructured":"Zhu, G., Pei, L., Xia, H., Tang, Q., Bi, F.: Role of oncogenic kras in the prognosis, diagnosis and treatment of colorectal cancer. Mol. Cancer 20, 143 (2021)","journal-title":"Mol. Cancer"},{"key":"5102_CR76","doi-asserted-by":"publisher","first-page":"djw332","DOI":"10.1093\/jnci\/djw332","volume":"109","author":"L Zhang","year":"2017","unstructured":"Zhang, L., Shay, J.W.: Multiple roles of apc and its therapeutic implications in colorectal cancer. JNCI: J. Natl. Cancer Inst. 109, djw332 (2017)","journal-title":"JNCI: J. Natl. Cancer Inst."},{"key":"5102_CR77","doi-asserted-by":"publisher","DOI":"10.1016\/j.tranon.2021.101221","volume":"14","author":"M Liang","year":"2021","unstructured":"Liang, M., et al.: Targeting matrix metalloproteinase mmp3 greatly enhances oncolytic virus mediated tumor therapy. Transl. Oncol. 14, 101221 (2021)","journal-title":"Transl. Oncol."},{"key":"5102_CR78","doi-asserted-by":"publisher","first-page":"2061","DOI":"10.1002\/cam4.772","volume":"5","author":"J Shen","year":"2016","unstructured":"Shen, J., et al.: Role of dusp1\/mkp1 in tumorigenesis, tumor progression and therapy. Cancer Med. 5, 2061\u20132068 (2016)","journal-title":"Cancer Med."},{"key":"5102_CR79","doi-asserted-by":"publisher","first-page":"1313","DOI":"10.1093\/carcin\/21.7.1313","volume":"21","author":"H Wang","year":"2000","unstructured":"Wang, H., Birkenbach, M., Hart, J.: Expression of jun family members in human colorectal adenocarcinoma. Carcinogenesis 21, 1313\u20131317 (2000)","journal-title":"Carcinogenesis"},{"key":"5102_CR80","doi-asserted-by":"publisher","first-page":"1565","DOI":"10.1007\/s00500-019-03988-3","volume":"24","author":"AM Anter","year":"2020","unstructured":"Anter, A.M., Ali, M.: Feature selection strategy based on hybrid crow search optimization algorithm integrated with chaos theory and fuzzy c-means algorithm for medical diagnosis problems. Soft. Comput. 24, 1565\u20131584 (2020)","journal-title":"Soft. Comput."},{"key":"5102_CR81","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.105858","volume":"148","author":"MH Nadimi-Shahraki","year":"2022","unstructured":"Nadimi-Shahraki, M.H., Zamani, H., Mirjalili, S.: Enhanced whale optimization algorithm for medical feature selection: a covid-19 case study. Comput. Biol. Med. 148, 105858 (2022)","journal-title":"Comput. Biol. Med."},{"key":"5102_CR82","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.104718","volume":"84","author":"RA Khurma","year":"2023","unstructured":"Khurma, R.A., et al.: An augmented snake optimizer for diseases and covid-19 diagnosis. Biomed. Signal Process. Control 84, 104718 (2023)","journal-title":"Biomed. Signal Process. Control"},{"key":"5102_CR83","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107629","volume":"235","author":"M Alweshah","year":"2022","unstructured":"Alweshah, M., Alkhalaileh, S., Al-Betar, M.A., Bakar, A.A.: Coronavirus herd immunity optimizer with greedy crossover for feature selection in medical diagnosis. Knowl.-Based Syst. 235, 107629 (2022)","journal-title":"Knowl.-Based Syst."},{"key":"5102_CR84","doi-asserted-by":"publisher","first-page":"7165","DOI":"10.1007\/s00521-020-05483-5","volume":"33","author":"Khurmaa R Abu","year":"2021","unstructured":"Abu, Khurmaa R., Aljarah, I., Sharieh, A.: An intelligent feature selection approach based on moth flame optimization for medical diagnosis. Neural Comput. Appl. 33, 7165\u20137204 (2021)","journal-title":"Neural Comput. Appl."},{"key":"5102_CR85","doi-asserted-by":"publisher","first-page":"6153","DOI":"10.1007\/s00521-022-08015-5","volume":"35","author":"M Braik","year":"2023","unstructured":"Braik, M.: Enhanced ali baba and the forty thieves algorithm for feature selection. Neural Comput. Appl. 35, 6153\u20136184 (2023)","journal-title":"Neural Comput. Appl."},{"key":"5102_CR86","doi-asserted-by":"crossref","unstructured":"Khurma, R.A., Aljarah, I., Sharieh, A.: Rank based moth flame optimisation for feature selection in the medical application. In: IEEE Congress on Evolutionary Computation (CEC), pp. 1\u20138 (2020)","DOI":"10.1109\/CEC48606.2020.9185498"},{"key":"5102_CR87","doi-asserted-by":"publisher","first-page":"8415","DOI":"10.1007\/s13369-021-05478-x","volume":"46","author":"RA Khurma","year":"2021","unstructured":"Khurma, R.A., Aljarah, I., Sharieh, A.: A simultaneous moth flame optimizer feature selection approach based on levy flight and selection operators for medical diagnosis. Arab. J. Sci. Eng. 46, 8415\u20138440 (2021)","journal-title":"Arab. J. Sci. Eng."},{"key":"5102_CR88","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.eswa.2016.01.021","volume":"53","author":"BZ Dadaneh","year":"2016","unstructured":"Dadaneh, B.Z., Markid, H.Y., Zakerolhosseini, A.: Unsupervised probabilistic feature selection using ant colony optimization. Expert Syst. Appl. 53, 27\u201342 (2016)","journal-title":"Expert Syst. Appl."},{"key":"5102_CR89","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1016\/j.neucom.2014.06.067","volume":"147","author":"S Kashef","year":"2015","unstructured":"Kashef, S., Nezamabadi-pour, H.: An advanced aco algorithm for feature subset selection. Neurocomputing 147, 271\u2013279 (2015)","journal-title":"Neurocomputing"},{"key":"5102_CR90","doi-asserted-by":"publisher","first-page":"2798","DOI":"10.1016\/j.patcog.2015.03.020","volume":"48","author":"S Tabakhi","year":"2015","unstructured":"Tabakhi, S., Moradi, P.: Relevance-redundancy feature selection based on ant colony optimization. Pattern Recogn. 48, 2798\u20132811 (2015)","journal-title":"Pattern Recogn."},{"key":"5102_CR91","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1080\/08839514.2020.1861407","volume":"35","author":"J Too","year":"2021","unstructured":"Too, J., Mirjalili, S.: General learning equilibrium optimizer: a new feature selection method for biological data classification. Appl. Artif. Intell. 35, 247\u2013263 (2021)","journal-title":"Appl. Artif. Intell."},{"key":"5102_CR92","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.compbiolchem.2015.03.001","volume":"56","author":"HM Alshamlan","year":"2015","unstructured":"Alshamlan, H.M., Badr, G.H., Alohali, Y.A.: Genetic bee colony (GBC) algorithm: a new gene selection method for microarray cancer classification. Comput. Biol. Chem. 56, 49\u201360 (2015)","journal-title":"Comput. Biol. Chem."},{"key":"5102_CR93","doi-asserted-by":"publisher","DOI":"10.1155\/2015\/604910","volume":"2015","author":"H Alshamlan","year":"2015","unstructured":"Alshamlan, H., Badr, G., Alohali, Y.: mrmr-abc: a hybrid gene selection algorithm for cancer classification using microarray gene expression profiling. Biomed. Res. Int. 2015, 604910 (2015)","journal-title":"Biomed. Res. Int."},{"key":"5102_CR94","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.knosys.2018.05.009","volume":"154","author":"H Faris","year":"2018","unstructured":"Faris, H., et al.: An efficient binary salp swarm algorithm with crossover scheme for feature selection problems. Knowl.-Based Syst. 154, 43\u201367 (2018)","journal-title":"Knowl.-Based Syst."},{"key":"5102_CR95","doi-asserted-by":"publisher","first-page":"2636","DOI":"10.1080\/03610918.2014.931971","volume":"44","author":"DG Pereira","year":"2015","unstructured":"Pereira, D.G., Afonso, A., Medeiros, F.M.: Overview of Friedman\u2019s test and post-hoc analysis. Commun. Stat.-Simul. Comput. 44, 2636\u20132653 (2015)","journal-title":"Commun. Stat.-Simul. Comput."}],"container-title":["Cluster Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-025-05102-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10586-025-05102-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-025-05102-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T07:25:54Z","timestamp":1757143554000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10586-025-05102-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,25]]},"references-count":95,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,8]]}},"alternative-id":["5102"],"URL":"https:\/\/doi.org\/10.1007\/s10586-025-05102-9","relation":{},"ISSN":["1386-7857","1573-7543"],"issn-type":[{"value":"1386-7857","type":"print"},{"value":"1573-7543","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,25]]},"assertion":[{"value":"29 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 January 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 January 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 February 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"250"}}