{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T15:05:49Z","timestamp":1753887949996,"version":"3.41.2"},"reference-count":22,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,3,16]],"date-time":"2021-03-16T00:00:00Z","timestamp":1615852800000},"content-version":"vor","delay-in-days":74,"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>In recent years, the prevalence of technological advances has led to an enormous and ever\u2010increasing amount of data that are now commonly available in a streaming fashion. In such nonstationary environments, the underlying process generating the data stream is characterized by an intrinsic nonstationary or evolving or drifting phenomenon known as concept drift. Given the increasingly common applications whose data generation mechanisms are susceptible to change, the need for effective and efficient algorithms for learning from and adapting to evolving or drifting environments can hardly be overstated. In dynamic environments associated with concept drift, learning models are frequently updated to adapt to changes in the underlying probability distribution of the data. A lot of work in the area of learning in nonstationary environments focuses on updating the learning predictive model to optimize recovery from concept drift and convergence to new concepts by adjusting parameters and discarding poorly performing models while little effort has been dedicated to investigate what type of learning model is suitable at any given time for different types of concept drift. In this paper, we investigate the impact of heterogeneous online ensemble learning based on online model selection for predictive modeling in dynamic environments. We propose a novel heterogeneous ensemble approach based on online dynamic ensemble selection that accurately interchanges between different types of base models in an ensemble to enhance its predictive performance in nonstationary environments. The approach is known as Heterogeneous Dynamic Ensemble Selection based on Accuracy and Diversity (HDES\u2010AD) and makes use of models generated by different base learners to increase diversity to circumvent problems associated with existing dynamic ensemble classifiers that may experience loss of diversity due to the exclusion of base learners generated by different base algorithms. The algorithm is evaluated on artificial and real\u2010world datasets with well\u2010known online homogeneous online ensemble approaches such as DDD, AFWE, and OAUE. The results show that HDES\u2010AD performed significantly better than the other three homogeneous online ensemble approaches in nonstationary environments.<\/jats:p>","DOI":"10.1155\/2021\/6669706","type":"journal-article","created":{"date-parts":[[2021,3,16]],"date-time":"2021-03-16T22:06:06Z","timestamp":1615932366000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["An Adaptive Heterogeneous Online Learning Ensemble Classifier for Nonstationary Environments"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0356-5245","authenticated-orcid":false,"given":"Tinofirei","family":"Museba","sequence":"first","affiliation":[]},{"given":"Fulufhelo","family":"Nelwamondo","sequence":"additional","affiliation":[]},{"given":"Khmaies","family":"Ouahada","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,3,16]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2016.03.045"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1108\/K-10-2016-0300"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1177\/0165551516677911"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/mci.2015.2471196"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2017.09.032"},{"key":"e_1_2_9_6_2","unstructured":"GrudicG. Z. MulliganJ. andProcopioM. An experimental analysis of classifier ensembles for learning drifting concepts over time in autonomous robot navigation 2002 University of Florida Gainesville FL USA Ph.D. thesis."},{"key":"e_1_2_9_7_2","doi-asserted-by":"crossref","unstructured":"PratamaM. ZainC. AshfahaniA. andSoonO. Y. Automotive construction of multi-layer perceptron network from streaming examples Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM 2019) August 2019 Beijing China https:\/\/doi.org\/10.1145\/3357384.3357946.","DOI":"10.1145\/3357384.3357946"},{"key":"e_1_2_9_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2013.05.048"},{"key":"e_1_2_9_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2017.10.022"},{"key":"e_1_2_9_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2018.2794503"},{"key":"e_1_2_9_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2019.01.002"},{"key":"e_1_2_9_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2017.09.009"},{"key":"e_1_2_9_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.104983"},{"key":"e_1_2_9_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2013.12.011"},{"key":"e_1_2_9_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/tkde.2011.58"},{"key":"e_1_2_9_16_2","doi-asserted-by":"publisher","DOI":"10.2991\/ijcis.11.1.33"},{"key":"e_1_2_9_17_2","doi-asserted-by":"crossref","unstructured":"AshfahaniA.andPratamaM. Autonomous deep learning: continual learning approach for dynamic environments Proceedings of the SIAM International Conference on Data Mining (SDMIG) 2018 Calgary Canada.","DOI":"10.1137\/1.9781611975673.75"},{"key":"e_1_2_9_18_2","doi-asserted-by":"publisher","DOI":"10.3233\/ida-2002-6203"},{"key":"e_1_2_9_19_2","doi-asserted-by":"publisher","DOI":"10.1098\/rsta.1900.0019"},{"key":"e_1_2_9_20_2","first-page":"1","article-title":"Statistical comparison of classifiers over multiple datasets","volume":"7","author":"Demsar J.","year":"2006","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_2_9_21_2","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1937.10503522"},{"key":"e_1_2_9_22_2","unstructured":"NemenyiP. Distribution free multiple comparisons 1963 Princetown University Baraily India Ph.D. thesis."}],"container-title":["Computational Intelligence and Neuroscience"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2021\/6669706.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2021\/6669706.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2021\/6669706","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,6]],"date-time":"2024-08-06T11:41:09Z","timestamp":1722944469000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2021\/6669706"}},"subtitle":[],"editor":[{"given":"Rodolfo E.","family":"Haber","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2021,1]]},"references-count":22,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["10.1155\/2021\/6669706"],"URL":"https:\/\/doi.org\/10.1155\/2021\/6669706","archive":["Portico"],"relation":{},"ISSN":["1687-5265","1687-5273"],"issn-type":[{"type":"print","value":"1687-5265"},{"type":"electronic","value":"1687-5273"}],"subject":[],"published":{"date-parts":[[2021,1]]},"assertion":[{"value":"2020-12-23","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-03-04","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-03-16","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"6669706"}}