{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T13:53:11Z","timestamp":1780581191506,"version":"3.54.1"},"reference-count":110,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,3,27]],"date-time":"2021-03-27T00:00:00Z","timestamp":1616803200000},"content-version":"vor","delay-in-days":85,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>The goal of aggregating the base classifiers is to achieve an aggregated classifier that has a higher resolution than individual classifiers. Random forest is one of the types of ensemble learning methods that have been considered more than other ensemble learning methods due to its simple structure, ease of understanding, as well as higher efficiency than similar methods. The ability and efficiency of classical methods are always influenced by the data. The capabilities of independence from the data domain, and the ability to adapt to problem space conditions, are the most challenging issues about the different types of classifiers. In this paper, a method based on learning automata is presented, through which the adaptive capabilities of the problem space, as well as the independence of the data domain, are added to the random forest to increase its efficiency. Using the idea of reinforcement learning in the random forest has made it possible to address issues with data that have a dynamic behaviour. Dynamic behaviour refers to the variability in the behaviour of a data sample in different domains. Therefore, to evaluate the proposed method, and to create an environment with dynamic behaviour, different domains of data have been considered. In the proposed method, the idea is added to the random forest using learning automata. The reason for this choice is the simple structure of the learning automata and the compatibility of the learning automata with the problem space. The evaluation results confirm the improvement of random forest efficiency.<\/jats:p>","DOI":"10.1155\/2021\/5572781","type":"journal-article","created":{"date-parts":[[2021,3,27]],"date-time":"2021-03-27T20:05:06Z","timestamp":1616875506000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":63,"title":["A New Random Forest Algorithm Based on Learning Automata"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5229-0589","authenticated-orcid":false,"given":"Mohammad","family":"Savargiv","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2314-283X","authenticated-orcid":false,"given":"Behrooz","family":"Masoumi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad Reza","family":"Keyvanpour","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,3,27]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4419-9326-7"},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2018.10.009"},{"key":"e_1_2_10_3_2","first-page":"1","article-title":"A new ensemble learning method based on learning automata","author":"Savargiv M.","year":"2020","journal-title":"Journal of Ambient Intelligence and Humanized Computing"},{"key":"e_1_2_10_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2017.07.019"},{"key":"e_1_2_10_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijar.2018.10.019"},{"key":"e_1_2_10_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-31150-6_13"},{"key":"e_1_2_10_7_2","unstructured":"MarkelJ.andBaylessA. J. Performance of random forest machine learning algorithms in binary supernovae classification 2019 http:\/\/arxiv.org\/abs\/1907.00088."},{"key":"e_1_2_10_8_2","doi-asserted-by":"publisher","DOI":"10.1080\/2150704x.2019.1682708"},{"key":"e_1_2_10_9_2","unstructured":"PangW. LiuX. WangZ. FanY. andWangJ. Predicting RNA molecular specific hybridization via random forest Proceedings of the 2019 IEEE 7th International Conference on Bioinformatics and Computational Biology (ICBCB) 2019 Hangzhou China 35\u201338."},{"key":"e_1_2_10_10_2","doi-asserted-by":"publisher","DOI":"10.11591\/ijece.v10i1.pp549-558"},{"key":"e_1_2_10_11_2","doi-asserted-by":"publisher","DOI":"10.3390\/en13020443"},{"key":"e_1_2_10_12_2","doi-asserted-by":"crossref","unstructured":"DaiJ. WangT. andWangS. A deep forest method for classifying e-commerce products by using title information Proceedings of the 2020 International Conference On Computing Networking And Communications (ICNC) 2020 Big Island HI USA 1\u20135.","DOI":"10.1109\/ICNC47757.2020.9049751"},{"key":"e_1_2_10_13_2","doi-asserted-by":"crossref","unstructured":"Papou\u0161kov\u00e1M.andHajekP. Modelling loss given default in peer-to-peer lending using random forests Proceedings of the Intelligent Decision Technologies 2019 2020 Malta Europe Springer 133\u2013141.","DOI":"10.1007\/978-981-13-8311-3_12"},{"key":"e_1_2_10_14_2","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.3551557"},{"key":"e_1_2_10_15_2","volume-title":"Price Dynamics on Amazon Marketplace: A Multivariate Random Forest Variable Selection Approach","author":"Sikdar S.","year":"2019"},{"key":"e_1_2_10_16_2","volume-title":"Pruning Random Forest with Orthogonal Matching Trees","author":"Giffon L.","year":"2020"},{"key":"e_1_2_10_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.chemolab.2019.01.002"},{"key":"e_1_2_10_18_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-15-0978-0_19"},{"key":"e_1_2_10_19_2","first-page":"1","article-title":"A hybrid model of convolutional neural networks and deep regression forests for crowd counting","author":"Ji Q.","year":"2020","journal-title":"Applied Intelligence"},{"key":"e_1_2_10_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2019.12.023"},{"key":"e_1_2_10_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.113072"},{"key":"e_1_2_10_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2019.107078"},{"key":"e_1_2_10_23_2","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1301"},{"key":"e_1_2_10_24_2","doi-asserted-by":"crossref","unstructured":"KimS. JeongM. andKoB. C. Interpretation and simplification of deep forest 2020 http:\/\/arxiv.org\/abs\/2001.04721.","DOI":"10.36227\/techrxiv.11661246.v1"},{"key":"e_1_2_10_25_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-34869-4_19"},{"key":"e_1_2_10_26_2","doi-asserted-by":"publisher","DOI":"10.3390\/atmos11030239"},{"key":"e_1_2_10_27_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2019.111501"},{"key":"e_1_2_10_28_2","first-page":"147","article-title":"Land-subsidence spatial modeling using the random forest data-mining technique","author":"Breidenbach H. R.","year":"2019","journal-title":"Spatial Modeling In GIS and R for Earth And Environmental Sciences"},{"key":"e_1_2_10_29_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2020.114566"},{"key":"e_1_2_10_30_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2020.137194"},{"key":"e_1_2_10_31_2","doi-asserted-by":"crossref","unstructured":"LiY.-S. ChiH. ShaoX.-Y. QiM.-L. andXuB.-G. A novel random forest approach for imbalance problem in crime linkage 2020 Knowledge-Based System 105738.","DOI":"10.1016\/j.knosys.2020.105738"},{"key":"e_1_2_10_32_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-13-9330-3_20"},{"key":"e_1_2_10_33_2","doi-asserted-by":"crossref","unstructured":"JoshiA. ChoudhuryT. Sai SabithaA. andSrujan RajuK. Data mining in healthcare and predicting obesity Proceedings of the Third International Conference on Computational Intelligence and Informatics 2020 Hyderabad India 877\u2013888 https:\/\/doi.org\/10.1007\/978-981-15-1480-7_82.","DOI":"10.1007\/978-981-15-1480-7_82"},{"key":"e_1_2_10_34_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2020.10.005"},{"key":"e_1_2_10_35_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-020-01770-9"},{"key":"e_1_2_10_36_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-33966-1_7"},{"key":"e_1_2_10_37_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.actbio.2020.02.037"},{"key":"e_1_2_10_38_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bbe.2019.04.004"},{"key":"e_1_2_10_39_2","article-title":"Computational modeling and analysis to predict intracellular parasite epitope characteristics using random forest technique","volume":"49","author":"Javadi A.","year":"2020","journal-title":"Journal of Public Health"},{"key":"e_1_2_10_40_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2017.01.012"},{"key":"e_1_2_10_41_2","first-page":"1","article-title":"Kalman filter based short term prediction model for COVID-19 spread","author":"Singh K. K.","year":"2020","journal-title":"Applied Intelligence"},{"key":"e_1_2_10_42_2","first-page":"9","article-title":"A study on random forest-based estimation model for changing the automatic walking mode of above knee prosthesis","volume":"24","author":"Na S.-J.","year":"2020","journal-title":"The Journal of IKEEE"},{"key":"e_1_2_10_43_2","first-page":"113","article-title":"Prospects of machine and deep learning in analysis of vital signs for the improvement of healthcare services","author":"Alloghani M.","year":"2020","journal-title":"Nature-Inspired Computation In Data Mining And Machine Learning"},{"key":"e_1_2_10_44_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2020.101811"},{"key":"e_1_2_10_45_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2020.2973843"},{"key":"e_1_2_10_46_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-15-1465-4_46"},{"key":"e_1_2_10_47_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2018.05.035"},{"key":"e_1_2_10_48_2","article-title":"Performance evaluation of random forest with feature selection methods in prediction of diabetes","volume":"10","author":"Guanter S.","year":"2020","journal-title":"International Journal of Electrical and Computer Engineering"},{"key":"e_1_2_10_49_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2018.12.011"},{"key":"e_1_2_10_50_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10260-018-0423-5"},{"key":"e_1_2_10_51_2","doi-asserted-by":"publisher","DOI":"10.1007\/s40846-019-00505-7"},{"key":"e_1_2_10_52_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.geoderma.2019.114139"},{"key":"e_1_2_10_53_2","doi-asserted-by":"publisher","DOI":"10.1080\/00032719.2019.1681439"},{"key":"e_1_2_10_54_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11053-019-09510-8"},{"key":"e_1_2_10_55_2","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/9206579"},{"key":"e_1_2_10_56_2","first-page":"1","article-title":"An empirical study of ensemble techniques for software fault prediction","author":"Rathore S. S.","year":"2020","journal-title":"Applied Intelligence"},{"key":"e_1_2_10_57_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.scs.2018.12.013"},{"key":"e_1_2_10_58_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2019.107224"},{"key":"e_1_2_10_59_2","doi-asserted-by":"publisher","DOI":"10.3390\/en13071786"},{"key":"e_1_2_10_60_2","doi-asserted-by":"publisher","DOI":"10.1061\/(asce)cp.1943-5487.0000796"},{"key":"e_1_2_10_61_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.foodcont.2019.106807"},{"key":"e_1_2_10_62_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.agrformet.2019.107808"},{"key":"e_1_2_10_63_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.datak.2019.101785"},{"key":"e_1_2_10_64_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-017-0907-2"},{"key":"e_1_2_10_65_2","article-title":"Multi-domain entropy-random forest method for the fusion diagnosis of inter-shaft bearing faults with acoustic emission signals","volume":"22","author":"Tian J.","year":"2020","journal-title":"Entropy"},{"key":"e_1_2_10_66_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-13-7403-6_23"},{"key":"e_1_2_10_67_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-15-0184-5_16"},{"key":"e_1_2_10_68_2","doi-asserted-by":"crossref","unstructured":"FangY. XuY. HuangC. LiuL. andZhangL. Against malicious SSL\/TLS encryption: identify malicious traffic based on random forest Proceedings of the Fourth International Congress on Information And Communication Technology 2020 London UK 99\u2013115.","DOI":"10.1007\/978-981-32-9343-4_10"},{"key":"e_1_2_10_69_2","doi-asserted-by":"crossref","unstructured":"BhavaniT. T. RaoM. K. andReddyA. M. Network intrusion detection system using random forest and decision tree machine learning techniques Proceedings of the First International Conference On Sustainable Technologies For Computational Intelligence 2020 London UK 637\u2013643.","DOI":"10.1007\/978-981-15-0029-9_50"},{"key":"e_1_2_10_70_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-13-9042-5_75"},{"key":"e_1_2_10_71_2","article-title":"A novel strategy for quantitative analysis of soil pH via laser-induced breakdown spectroscopy coupled with random forest","volume":"22","author":"Mingjing Z.","year":"2020","journal-title":"Plasma Science Technology"},{"key":"e_1_2_10_72_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.orgel.2019.105465"},{"key":"e_1_2_10_73_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.conbuildmat.2019.03.189"},{"key":"e_1_2_10_74_2","first-page":"469","article-title":"Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization","volume":"12","author":"Nener W.","year":"2020","journal-title":"Geoscience Frontiers"},{"key":"e_1_2_10_75_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.enggeo.2019.105328"},{"key":"e_1_2_10_76_2","doi-asserted-by":"publisher","DOI":"10.1007\/s41133-020-00032-0"},{"key":"e_1_2_10_77_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2020.11.022"},{"key":"e_1_2_10_78_2","first-page":"112","article-title":"Sentiment analysis of a product based on user reviews using random forests algorithm","volume":"32","author":"Singh S. N.","year":"2020","journal-title":"Data Science & Engineering"},{"key":"e_1_2_10_79_2","article-title":"Label flipping attacks against Naive Bayes on spam filtering systems","author":"Zhang H.","year":"2021","journal-title":"Applied Intelligence"},{"key":"e_1_2_10_80_2","first-page":"1","article-title":"Newspaper text recognition of Gurumukhi script using random forest classifier","author":"Kaur R. P.","year":"2019","journal-title":"Multimedia Tools and Applications Journal"},{"key":"e_1_2_10_81_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2020.10.037"},{"key":"e_1_2_10_82_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-019-08409-z"},{"key":"e_1_2_10_83_2","doi-asserted-by":"publisher","DOI":"10.4018\/ijswis.2020040103"},{"key":"e_1_2_10_84_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-13-8759-3_5"},{"key":"e_1_2_10_85_2","article-title":"Weighted word embeddings and clustering-based identification of question topics in MOOC discussion forum posts","author":"Onan A.","year":"2020","journal-title":"Computer Applications in Engineering Education"},{"key":"e_1_2_10_86_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2019.111606"},{"key":"e_1_2_10_87_2","article-title":"Sentiment analysis on massive open online course evaluations: a text mining and deep learning approach","author":"Beck A.","year":"2020","journal-title":"Computer Applications in Engineering Education"},{"key":"e_1_2_10_88_2","article-title":"Sentiment analysis on product reviews based on weighted word embeddings and deep neural networks","author":"Onan A.","year":"2020","journal-title":"Computer Applications in Engineering Education"},{"key":"e_1_2_10_89_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2021.3049734"},{"key":"e_1_2_10_90_2","doi-asserted-by":"publisher","DOI":"10.4018\/978-1-7998-0106-1.ch014"},{"key":"e_1_2_10_91_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-15-0077-0_17"},{"key":"e_1_2_10_92_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-13-8676-3_11"},{"key":"e_1_2_10_93_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ufug.2019.126561"},{"key":"e_1_2_10_94_2","doi-asserted-by":"publisher","DOI":"10.1080\/03081060.2019.1622250"},{"key":"e_1_2_10_95_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.scs.2020.102045"},{"key":"e_1_2_10_96_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asr.2019.09.035"},{"key":"e_1_2_10_97_2","doi-asserted-by":"publisher","DOI":"10.1049\/iet-its.2019.0414"},{"key":"e_1_2_10_98_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-020-01111-9"},{"key":"e_1_2_10_99_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-019-00978-7"},{"key":"e_1_2_10_100_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-018-00916-z"},{"key":"e_1_2_10_101_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-020-01136-0"},{"key":"e_1_2_10_102_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-019-01627-w"},{"key":"e_1_2_10_103_2","volume-title":"Learning Automata: An Introduction","author":"Narendra K. S.","year":"2012"},{"key":"e_1_2_10_104_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-72428-7"},{"key":"e_1_2_10_105_2","volume-title":"Reinforcement Learning: An Introduction","author":"Sutton R. S.","year":"2017"},{"key":"e_1_2_10_106_2","doi-asserted-by":"crossref","unstructured":"PangB.andLeeL. Seeing stars: exploiting class relationships for sentiment categorization with respect to rating scales 2005 http:\/\/arxiv.org\/abs\/0506075.","DOI":"10.3115\/1219840.1219855"},{"key":"e_1_2_10_107_2","unstructured":"MaasA. DalyR. E. PhamP. T. HuangD. NgA. Y. andPottsC. Learning word vectors for sentiment analysis Proceedings Of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies 2011 Buffalo NY USA 142\u2013150."},{"key":"e_1_2_10_108_2","doi-asserted-by":"crossref","unstructured":"PangB. LeeL. andVaithyanathanS. Thumbs up? sentiment classification using machine learning techniques 2002 http:\/\/arxiv.org\/abs\/0205070.","DOI":"10.3115\/1118693.1118704"},{"key":"e_1_2_10_109_2","volume-title":"UCI Machine Learning Repository","author":"Dua D.","year":"2019"},{"key":"e_1_2_10_110_2","doi-asserted-by":"publisher","DOI":"10.1080\/03610926.2017.1408829"}],"container-title":["Computational Intelligence and Neuroscience"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2021\/5572781.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2021\/5572781.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2021\/5572781","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,6]],"date-time":"2024-08-06T11:57:24Z","timestamp":1722945444000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2021\/5572781"}},"subtitle":[],"editor":[{"given":"Nian","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2021,1]]},"references-count":110,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["10.1155\/2021\/5572781"],"URL":"https:\/\/doi.org\/10.1155\/2021\/5572781","archive":["Portico"],"relation":{},"ISSN":["1687-5265","1687-5273"],"issn-type":[{"value":"1687-5265","type":"print"},{"value":"1687-5273","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1]]},"assertion":[{"value":"2021-02-12","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-03-16","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-03-27","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"5572781"}}