{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T15:15:32Z","timestamp":1768403732471,"version":"3.49.0"},"reference-count":43,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2020,9,29]],"date-time":"2020-09-29T00:00:00Z","timestamp":1601337600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Next-Generation Information Computing Development Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT","award":["NRF-2017M3C4A7069440"],"award-info":[{"award-number":["NRF-2017M3C4A7069440"]}]},{"name":"Institute of Information &amp; communications Technology Planning &amp; Evaluation(IITP) grant funded by the Korea government(MSIT) (No.2019-0-00421, Artificial Intelligence Graduate School Program(Sungkyunkwan University))","award":["No.2019-0-00421"],"award-info":[{"award-number":["No.2019-0-00421"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>The complexity and high dimensionality are the inherent concerns of big data. The role of feature selection has gained prime importance to cope with the issue by reducing dimensionality of datasets. The compromise between the maximum classification accuracy and the minimum dimensions is as yet an unsolved puzzle. Recently, Monte Carlo Tree Search (MCTS)-based techniques have been invented that have attained great success in feature selection by constructing a binary feature selection tree and efficiently focusing on the most valuable features in the features space. However, one challenging problem associated with such approaches is a tradeoff between the tree search and the number of simulations. In a limited number of simulations, the tree might not meet the sufficient depth, thus inducing biasness towards randomness in feature subset selection. In this paper, a new algorithm for feature selection is proposed where multiple feature selection trees are built iteratively in a recursive fashion. The state space of every successor feature selection tree is less than its predecessor, thus increasing the impact of tree search in selecting best features, keeping the MCTS simulations fixed. In this study, experiments are performed on 16 benchmark datasets for validation purposes. We also compare the performance with state-of-the-art methods in literature both in terms of classification accuracy and the feature selection ratio.<\/jats:p>","DOI":"10.3390\/e22101093","type":"journal-article","created":{"date-parts":[[2020,9,29]],"date-time":"2020-09-29T08:43:27Z","timestamp":1601369007000},"page":"1093","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Monte Carlo Tree Search-Based Recursive Algorithm for Feature Selection in High-Dimensional Datasets"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7287-2372","authenticated-orcid":false,"given":"Muhammad Umar","family":"Chaudhry","sequence":"first","affiliation":[{"name":"AiHawks, Multan 60000, Pakistan"},{"name":"Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7106-6598","authenticated-orcid":false,"given":"Muhammad","family":"Yasir","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Engineering and Technology Lahore, Faisalabad Campus, Faisalabad 38000, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9487-4344","authenticated-orcid":false,"given":"Muhammad Nabeel","family":"Asghar","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Bahauddin Zakariya University, Multan 60000, Pakistan"}]},{"given":"Jee-Hyong","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"860","DOI":"10.3390\/e13040860","article-title":"A feature subset selection method based on high-dimensional mutual information","volume":"13","author":"Zheng","year":"2011","journal-title":"Entropy"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Sluga, D., and Lotri\u010d, U. (2017). Quadratic mutual information feature selection. Entropy, 19.","DOI":"10.3390\/e19040157"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1664","DOI":"10.1016\/j.patcog.2013.10.009","article-title":"Efficient feature size reduction via predictive forward selection","volume":"47","author":"Reif","year":"2014","journal-title":"Pattern Recognit."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3053","DOI":"10.3390\/e17053053","article-title":"Predicting community evolution in social networks","volume":"17","author":"Saganowski","year":"2015","journal-title":"Entropy"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Smieja, M., and Warszycki, D. (2016). Average information content maximization-a new approach for fingerprint hybridization and reduction. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0146666"},{"key":"ref_6","first-page":"337","article-title":"The Elements of Statistical Learning","volume":"1","author":"Hastie","year":"2009","journal-title":"Elements"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.csda.2014.02.005","article-title":"Group subset selection for linear regression","volume":"75","author":"Guo","year":"2014","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_8","unstructured":"Dash, M., Choi, K., Scheuermann, P., and Liu, H. (2002, January 9\u201312). Feature selection for clustering\u2014A filter solution. Proceedings of the 2002 IEEE International Conference on Data Mining, Maebashi, Japan."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Kim, Y., Street, W.N., and Menczer, F. (2000, January 20\u201323). Feature selection in unsupervised learning via evolutionary search. Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Boston, MA, USA.","DOI":"10.1145\/347090.347169"},{"key":"ref_10","first-page":"1157","article-title":"An introduction to variable and feature selection","volume":"3","author":"Iguyon","year":"2003","journal-title":"J. Mach. Learn. Res."},{"key":"ref_11","first-page":"1","article-title":"Correlation-based Feature Selection for Machine Learning","volume":"21i195-i20","author":"Hall","year":"1999","journal-title":"Methodology"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.patcog.2017.01.026","article-title":"A new maximum relevance-minimum multicollinearity (MRmMC) method for feature selection and ranking","volume":"67","author":"Senawi","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1016\/j.neucom.2014.09.027","article-title":"Effective feature selection using feature vector graph for classification","volume":"151","author":"Zhao","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1016\/j.patcog.2018.02.020","article-title":"Class-specific mutual information variation for feature selection","volume":"79","author":"Gao","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.eswa.2018.05.029","article-title":"Feature selection by integrating two groups of feature evaluation criteria","volume":"110","author":"Gao","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.patrec.2018.06.005","article-title":"Feature selection considering the composition of feature relevancy","volume":"112","author":"Gao","year":"2018","journal-title":"Pattern Recognit. Lett."},{"key":"ref_17","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_18","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_19","doi-asserted-by":"crossref","first-page":"2501","DOI":"10.1016\/j.asoc.2010.08.020","article-title":"Hierarchical genetic algorithm with new evaluation function and bi-coded representation for the selection of features considering their confidence rate","volume":"11","author":"Hamdani","year":"2011","journal-title":"Appl. Soft Comput."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.patrec.2005.07.009","article-title":"Efficient huge-scale feature selection with speciated genetic algorithm","volume":"27","author":"Hong","year":"2006","journal-title":"Pattern Recognit. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"4625","DOI":"10.1016\/j.ins.2010.05.037","article-title":"Mr2PSO: A maximum relevance minimum redundancy feature selection method based on swarm intelligence for support vector machine classification","volume":"181","author":"Unler","year":"2011","journal-title":"Inf. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.neucom.2012.09.049","article-title":"Feature selection algorithm based on bare bones particle swarm optimization","volume":"148","author":"Zhang","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1656","DOI":"10.1109\/TSMCB.2012.2227469","article-title":"Particle swarm optimization for feature selection in classification: A multi-objective approach","volume":"43","author":"Xue","year":"2013","journal-title":"IEEE Trans. Cybern."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3747","DOI":"10.1016\/j.eswa.2011.09.073","article-title":"A new hybrid ant colony optimization algorithm for feature selection","volume":"39","author":"Kabir","year":"2012","journal-title":"Expert Syst. Appl."},{"key":"ref_25","unstructured":"Wang, H., Meng, Y., Yin, P., and Hua, J. (2016, January 26\u201328). A Model-Driven Method for Quality Reviews Detection: An Ensemble Model of Feature Selection. Proceedings of the 15th Wuhan International Conference on E-Business (WHICEB 2016), Wuhan, China."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"634","DOI":"10.1016\/j.asoc.2018.10.036","article-title":"Feature selection based on artificial bee colony and gradient boosting decision tree","volume":"74","author":"Rao","year":"2019","journal-title":"Appl. Soft Comput. J."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Chaudhry, M.U., and Lee, J.-H. (2018). MOTiFS: Monte Carlo Tree Search Based Feature Selection. Entropy, 20.","DOI":"10.3390\/e20050385"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"76036","DOI":"10.1109\/ACCESS.2018.2883537","article-title":"Feature selection for high dimensional data using monte carlo tree search","volume":"6","author":"Chaudhry","year":"2018","journal-title":"IEEE Access"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TCIAIG.2012.2186810","article-title":"A survey of monte carlo tree search methods","volume":"4","author":"Browne","year":"2012","journal-title":"IEEE Trans. Intell. AI Games"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1038\/nature16961","article-title":"Mastering the game of Go with deep neural networks and tree search","volume":"529","author":"Silver","year":"2016","journal-title":"Nature"},{"key":"ref_31","unstructured":"Gaudel, R., and Sebag, M. (2010, January 21\u201324). Feature Selection as a One-Player Game. Proceedings of the 27th International Conference on International Conference on Machine Learning, Haifa, Israel."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1892","DOI":"10.1016\/j.camwa.2013.06.031","article-title":"Using reinforcement learning to find an optimal set of features","volume":"66","author":"Hazrati","year":"2013","journal-title":"Comput. Math. Appl."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1016\/j.neucom.2014.02.001","article-title":"Bandit-based local feature subset selection","volume":"138","year":"2014","journal-title":"Neurocomputing"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1080\/21642583.2019.1661312","article-title":"An improved relief feature selection algorithm based on Monte-Carlo tree search","volume":"7","author":"Zheng","year":"2019","journal-title":"Syst. Sci. Control Eng."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2336","DOI":"10.1016\/j.eswa.2014.10.044","article-title":"Sequential random k-nearest neighbor feature selection for high-dimensional data","volume":"42","author":"Park","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1371","DOI":"10.1214\/aos\/1176325633","article-title":"On the Strong Universal Consistency of Nearest Neighbor Regression Function Estimates","volume":"22","author":"Devroye","year":"1994","journal-title":"Ann. Stat."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1007\/BF00153759","article-title":"Instance-Based Learning Algorithms","volume":"6","author":"Aha","year":"1991","journal-title":"Mach. Learn."},{"key":"ref_38","unstructured":"Machine Learning Repository (2019, September 10). Retrieved from University of California, Irvine. Available online: http:\/\/archive.ics.uci.edu\/ml\/index.php."},{"key":"ref_39","unstructured":"Chang, C., and Lin, C. (2019, September 10). Retrieved from LIBSVM\u2014A Library for Support Vector Machines. Available online: https:\/\/www.csie.ntu.edu.tw\/~cjlin\/libsvm\/."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.patrec.2015.07.007","article-title":"Simultaneous feature selection and weighting\u2014An evolutionary multi-objective optimization approach","volume":"65","author":"Paul","year":"2015","journal-title":"Pattern Recognit. Lett."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.knosys.2017.02.013","article-title":"Ensemble feature selection using bi-objective genetic algorithm","volume":"123","author":"Das","year":"2017","journal-title":"Knowl.-Based Syst."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/j.asoc.2013.09.018","article-title":"Particle swarm optimisation for feature selection in classification: Novel initialisation and updating mechanisms","volume":"18","author":"Xue","year":"2014","journal-title":"Appl. Soft Comput. J."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1016\/j.asoc.2017.11.006","article-title":"Whale optimization approaches for wrapper feature selection","volume":"62","author":"Mafarja","year":"2018","journal-title":"Appl. Soft Comput. J."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/22\/10\/1093\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:14:42Z","timestamp":1760177682000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/22\/10\/1093"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,29]]},"references-count":43,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2020,10]]}},"alternative-id":["e22101093"],"URL":"https:\/\/doi.org\/10.3390\/e22101093","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,29]]}}}