{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T07:14:25Z","timestamp":1777446865499,"version":"3.51.4"},"reference-count":53,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,9,28]],"date-time":"2021-09-28T00:00:00Z","timestamp":1632787200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Sanming University Introduces High-level Talents to Start Scientific Research Funding Support Project","award":["21YG01S"],"award-info":[{"award-number":["21YG01S"]}]},{"name":"Bidding project for higher education research of Sanming University","award":["SHE2101"],"award-info":[{"award-number":["SHE2101"]}]},{"name":"the Guiding Science and Technology Projects in Sanming City","award":["2021-S-8"],"award-info":[{"award-number":["2021-S-8"]}]},{"name":"the Educational Research Projects of Young and Middle-aged Teachers in Fujian Province","award":["JAT200618"],"award-info":[{"award-number":["JAT200618"]}]},{"name":"the Scientific Research and Development Fund of Sanming University","award":["B202009"],"award-info":[{"award-number":["B202009"]}]},{"name":"Open Research Fund of Key Laboratory of Agricultural Internet of Things in Fujian Province","award":["ZD2101"],"award-info":[{"award-number":["ZD2101"]}]},{"name":"Ministry of Education Cooperative Education Project","award":["202002064014"],"award-info":[{"award-number":["202002064014"]}]},{"name":"School level education and teaching reform project of Sanming University","award":["J2010305"],"award-info":[{"award-number":["J2010305"]}]},{"name":"Higher education research project of Sanming University","award":["SHE2013"],"award-info":[{"award-number":["SHE2013"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Chimp Optimization Algorithm (ChOA), a novel meta-heuristic algorithm, has been proposed in recent years. It divides the population into four different levels for the purpose of hunting. However, there are still some defects that lead to the algorithm falling into the local optimum. To overcome these defects, an Enhanced Chimp Optimization Algorithm (EChOA) is developed in this paper. Highly Disruptive Polynomial Mutation (HDPM) is introduced to further explore the population space and increase the population diversity. Then, the Spearman\u2019s rank correlation coefficient between the chimps with the highest fitness and the lowest fitness is calculated. In order to avoid the local optimization, the chimps with low fitness values are introduced with Beetle Antenna Search Algorithm (BAS) to obtain visual ability. Through the introduction of the above three strategies, the ability of population exploration and exploitation is enhanced. On this basis, this paper proposes an EChOA-SVM model, which can optimize parameters while selecting the features. Thus, the maximum classification accuracy can be achieved with as few features as possible. To verify the effectiveness of the proposed method, the proposed method is compared with seven common methods, including the original algorithm. Seventeen benchmark datasets from the UCI machine learning library are used to evaluate the accuracy, number of features, and fitness of these methods. Experimental results show that the classification accuracy of the proposed method is better than the other methods on most data sets, and the number of features required by the proposed method is also less than the other algorithms.<\/jats:p>","DOI":"10.3390\/a14100282","type":"journal-article","created":{"date-parts":[[2021,9,28]],"date-time":"2021-09-28T12:29:14Z","timestamp":1632832154000},"page":"282","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Simultaneous Feature Selection and Support Vector Machine Optimization Using an Enhanced Chimp Optimization Algorithm"],"prefix":"10.3390","volume":"14","author":[{"given":"Di","family":"Wu","sequence":"first","affiliation":[{"name":"School of Education and Music, Sanming University, Sanming 365004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wanying","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4339-8464","authenticated-orcid":false,"given":"Heming","family":"Jia","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Sanming University, Sanming 365004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Leng","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Raju, B., and Bonagiri, R. (2020). A cavernous analytics using advanced machine learning for real world datasets in research implementations. Mater. Today Proc.","DOI":"10.1016\/j.matpr.2020.11.089"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3382","DOI":"10.3934\/mbe.2020191","article-title":"Achieving better connections between deposited lines in additive manufacturing via machine learning","volume":"17","author":"Jiang","year":"2020","journal-title":"Math. Biosci. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1007\/s11517-020-02301-x","article-title":"Item response theory as a feature selection and interpretation tool in the context of machine learning","volume":"59","author":"Kline","year":"2021","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_4","first-page":"1","article-title":"Predicting the number of dusty days around the desert wetlands in southeastern Iran using feature selection and machine learning techniques","volume":"125","author":"Nafarzadegan","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1007\/s12559-014-9278-8","article-title":"Robust and Sparse Linear Programming Twin Support Vector Machines","volume":"7","author":"Tanveer","year":"2015","journal-title":"Cogn. Comput."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"386","DOI":"10.3934\/mbe.2021021","article-title":"Studies on fault diagnosis of dissolved oxygen sensor based on GA-SVM","volume":"18","author":"Yang","year":"2021","journal-title":"Math. Biosci. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"495","DOI":"10.3934\/mbe.2021027","article-title":"Machine learning based classification of normal, slow and fast walking by extracting multimodal features from stride interval time series","volume":"18","author":"Aziz","year":"2021","journal-title":"Math. Biosci. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"69","DOI":"10.3934\/mbe.2021004","article-title":"Machine learning based congestive heart failure detection using feature importance ranking of multimodal features","volume":"18","author":"Hussain","year":"2021","journal-title":"Math. Biosci. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1073\/pnas.97.1.262","article-title":"Knowledge-based analysis of microarray gene expression data by using support vector machines","volume":"97","author":"Brown","year":"2000","journal-title":"Proc. Natl. Acad. Sci. USA."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.artmed.2004.07.019","article-title":"Bio-medical entity extraction using support vector machines","volume":"33","author":"Takeuchi","year":"2005","journal-title":"Artif. Intell. Med."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2182","DOI":"10.1016\/j.eswa.2009.07.055","article-title":"Effects of principle component analysis on assessment of coronary artery diseases using support vector machine","volume":"37","author":"Findik","year":"2010","journal-title":"Expert Syst. Appl."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1838","DOI":"10.3934\/mbe.2020097","article-title":"A comprehensive health classification model based on support vector machine for proseal laryngeal mask and tracheal catheter assessment in herniorrhaphy","volume":"17","author":"Du","year":"2020","journal-title":"Math. Biosci. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1109\/TR.2010.2048740","article-title":"Anomaly Detection through a Bayesian Support Vector Machine","volume":"59","author":"Sotiris","year":"2010","journal-title":"IEEE Trans. Reliab."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"911","DOI":"10.1007\/s10596-020-10030-1","article-title":"Optimal feature selection for SAR image classification using biogeography-based optimization (BBO), artificial bee colony (ABC) and support vector machine (SVM): A combined approach of optimization and machine learning","volume":"25","author":"Rostami","year":"2021","journal-title":"Comput. Geosci."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Joachims, T. (1999). Making Large-Scale Support Vector Machine Learning Practical, MIT Press.","DOI":"10.7551\/mitpress\/1130.003.0015"},{"key":"ref_16","first-page":"668","article-title":"Feature selection for SVMs","volume":"13","author":"Weston","year":"2000","journal-title":"Adv. Neural Inf. Process Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"584","DOI":"10.1016\/j.patcog.2009.09.003","article-title":"Optimal feature selection for support vector machines","volume":"43","author":"Nguyen","year":"2010","journal-title":"Pattern Recognit."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.sigpro.2016.07.035","article-title":"A fuzzy multi-objective hybrid TLBO\u2013PSO approach to select the associated genes with breast cancer","volume":"131","author":"Shahbeig","year":"2017","journal-title":"Signal Process."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"80588","DOI":"10.1109\/ACCESS.2019.2919956","article-title":"A feature selection method based on hybrid improved binary quantum particle swarm optimization","volume":"7","author":"Wu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1504\/IJBIC.2018.091234","article-title":"Feature Selection based on Binary Particle Swarm Optimization and Neural Networks for Pathological Voice Detection","volume":"11","author":"Souza","year":"2018","journal-title":"Int. J. Bio-Inspired Comput."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"747","DOI":"10.1016\/j.neucom.2020.07.142","article-title":"Bacterial colony algorithm with adaptive attribute learning strategy for feature selection in classification of customers for personalized recommendation","volume":"452","author":"Wang","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"106823","DOI":"10.1016\/j.asoc.2020.106823","article-title":"Incorporation of multimodal objective optimization in designing a filter based feature selection technique","volume":"98","author":"Jha","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.neucom.2012.12.006","article-title":"Feature selection techniques with class separability for multivariate time series","volume":"110","author":"Han","year":"2013","journal-title":"Neurocomputing"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1007\/s42452-019-0645-7","article-title":"Evaluation of machine learning based optimized feature selection approaches and classification methods for cervical cancer prediction","volume":"1","author":"Nithya","year":"2019","journal-title":"SN Appl. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/S0004-3702(97)00043-X","article-title":"Wrappers for feature subset selection","volume":"97","author":"Kohavi","year":"1997","journal-title":"Artif. Intell."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1016\/j.neucom.2019.01.011","article-title":"A hybrid model of fuzzy min\u2013max and brain storm optimization for feature selection and data classification","volume":"333","author":"Pourpanah","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_27","unstructured":"Liu, H., and Setiono, R. (1996, January 4\u20137). A probabilistic approach to feature selection-a filter solution. Proceedings of the 9th International Conference on Industrial and Engineering Applications of AI and ES, Fukuoka, Japan."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1616","DOI":"10.1016\/j.patcog.2012.11.025","article-title":"Maximum weight and minimum redundancy: A novel framework for feature subset selection","volume":"46","author":"Wang","year":"2013","journal-title":"Pattern Recognit."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"121127","DOI":"10.1109\/ACCESS.2020.3006473","article-title":"Improved Harris Hawks Optimization Using Elite Opposition-Based Learning and Novel Search Mechanism for Feature Selection","volume":"8","author":"Sihwail","year":"2020","journal-title":"IEEE Access"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"186638","DOI":"10.1109\/ACCESS.2020.3029728","article-title":"An Improved Harris Hawks Optimization Algorithm with Simulated Annealing for Feature Selection in the Medical Field","volume":"8","author":"Elgamal","year":"2020","journal-title":"IEEE Access"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"113338","DOI":"10.1016\/j.eswa.2020.113338","article-title":"Chimp optimization algorithm","volume":"149","author":"Khishe","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_32","first-page":"57","article-title":"Island-based Cuckoo Search with Highly Disruptive Polynomial Mutation","volume":"17","year":"2019","journal-title":"Int. J. Artif. Intell."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Jiang, X., and Li, S. (2017). BAS: Beetle Antennae Search Algorithm for Optimization Problems. arXiv.","DOI":"10.5430\/ijrc.v1n1p1"},{"key":"ref_34","unstructured":"Lichman, M. (2013, August 15). UCI Machine Learning Repository. Available online: http:\/\/archive.ics.uci.edu\/ml."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"17227","DOI":"10.1007\/s11042-019-07777-w","article-title":"Novel real time content based medical image retrieval scheme with GWO-SVM","volume":"79","author":"Renita","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"022140","DOI":"10.1088\/1742-6596\/1237\/2\/022140","article-title":"A novel SVM parameter tuning method based on advanced whale optimization algorithm","volume":"1237","author":"Yin","year":"2019","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_37","first-page":"722","article-title":"Ant Lion Optimizer with Chaotic Investigation Mechanism for Optimizing SVM Parameters","volume":"10","author":"Zhao","year":"2016","journal-title":"J. Front. Comput. Sci. Technol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"478","DOI":"10.1007\/s12559-017-9542-9","article-title":"Simultaneous Feature Selection and Support Vector Machine Optimization Using the Grasshopper Optimization Algorithm","volume":"10","author":"Aljarah","year":"2018","journal-title":"Cogn. Comput."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"119966","DOI":"10.1016\/j.jclepro.2020.119966","article-title":"An improved moth-flame optimization algorithm for support vector machine prediction of photovoltaic power generation","volume":"253","author":"Lin","year":"2020","journal-title":"J. Clean. Prod."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"141","DOI":"10.2166\/hydro.2001.0014","article-title":"Rainfall and runoff forecasting with SSA\u2013SVM approach","volume":"3","author":"Sivapragasam","year":"2001","journal-title":"J. Hydroinformatics"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ins.2019.08.069","article-title":"An online-learning-based evolutionary many-objective algorithm","volume":"509","author":"Zhao","year":"2020","journal-title":"Inf. Sci."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1016\/j.ins.2021.02.039","article-title":"An Adaptive Polyploid Memetic Algorithm for scheduling trucks at a cross-docking terminal","volume":"565","author":"Dulebenets","year":"2021","journal-title":"Inf. Sci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1016\/j.ins.2018.06.063","article-title":"AnD: A many-objective evolutionary algorithm with angle-based selection and shift-based density estimation","volume":"509","author":"Liu","year":"2020","journal-title":"Inf. Sci."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"134743","DOI":"10.1109\/ACCESS.2020.3010176","article-title":"An Optimization Model and Solution Algorithms for the Vehicle Routing Problem with a \u201cFactory-in-a-Box\u201d","volume":"8","author":"Pasha","year":"2020","journal-title":"IEEE Access"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"11775","DOI":"10.1007\/s00500-018-03729-y","article-title":"A proposal for distinguishing between bacterial and viral meningitis using genetic programming and decision trees","volume":"23","author":"Pilla","year":"2019","journal-title":"Soft Comput."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Panda, N., and Majhi, S.K. (2020). How effective is the salp swarm algorithm in data classification. Computational Intelligence in Pattern Recognition, Springer.","DOI":"10.1007\/978-981-13-9042-5_49"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1504\/IJBIC.2017.081842","article-title":"Mixed-variable ant colony optimisation algorithm for feature subset selection and tuning support vector machine parameter","volume":"9","author":"Alwan","year":"2017","journal-title":"Int. J. Bio-Inspired Comput."},{"key":"ref_48","unstructured":"Frhlich, H., Chapelle, O., and Schlkopf, B. (2003, January 5). Feature Selection for Support Vector Machines by Means of Genetic Algorithms. Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2003), Sacramento, CA, USA."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.eswa.2005.09.024","article-title":"A GA-based feature selection and parameters optimizationfor support vector machines","volume":"31","author":"Huang","year":"2006","journal-title":"Expert Syst. Appl."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Lin, S.-W., Tseng, T.-Y., Chen, S.-C., and Huang, J.-F. (2006). A SA-Based Feature Selection and Parameter Optimization Approach for Support Vector Machine. Pervasive Comput. IEEE.","DOI":"10.1109\/ICSMC.2006.384599"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1249","DOI":"10.1007\/s10044-021-00985-x","article-title":"Improved barnacles mating optimizer algorithm for feature selection and support vector machine optimization","volume":"24","author":"Jia","year":"2021","journal-title":"Pattern Anal. Appl."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Slipinski, A., and Escalona, H. (2013). Australian Longhorn Beetles (Coleoptera: Cerambycidae), CSIRO Publishing.","DOI":"10.1071\/9780643109919"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1016\/j.neucom.2017.04.053","article-title":"Hybrid Whale Optimization Algorithm with simulated annealing for feature selection","volume":"260","author":"Mafarja","year":"2017","journal-title":"Neurocomputing"}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/14\/10\/282\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:06:37Z","timestamp":1760166397000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/14\/10\/282"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,28]]},"references-count":53,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2021,10]]}},"alternative-id":["a14100282"],"URL":"https:\/\/doi.org\/10.3390\/a14100282","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,28]]}}}