{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:10:33Z","timestamp":1760235033901,"version":"build-2065373602"},"reference-count":46,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,7,22]],"date-time":"2021-07-22T00:00:00Z","timestamp":1626912000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Classification algorithms have shown exceptional prediction results in the supervised learning area. These classification algorithms are not always efficient when it comes to real-life datasets due to class distributions. As a result, datasets for real-life applications are generally imbalanced. Several methods have been proposed to solve the problem of class imbalance. In this paper, we propose a hybrid method combining the preprocessing techniques and those of ensemble learning. The original training set is undersampled by evaluating the samples by stochastic measurement (SM) and then training these samples selected by Multilayer Perceptron to return a balanced training set. The MLPUS (Multilayer perceptron undersampling) balanced training set is aggregated using the bagging ensemble method. We applied our method to the real-life Niger_Rice dataset and forty-four other imbalanced datasets from the KEEL repository in this study. We also compared our method with six other existing methods in the literature, such as the MLP classifier on the original imbalance dataset, MLPUS, UnderBagging (combining random under-sampling and bagging), RUSBoost, SMOTEBagging (Synthetic Minority Oversampling Technique and bagging), SMOTEBoost. The results show that our method is competitive compared to other methods. The Niger_Rice real-life dataset results are 75.6, 0.73, 0.76, and 0.86, respectively, for accuracy, F-measure, G-mean, and ROC with our proposed method. In contrast, the MLP classifier on the original imbalance Niger_Rice dataset gives results 72.44, 0.82, 0.59, and 0.76 respectively for accuracy, F-measure, G-mean, and ROC.<\/jats:p>","DOI":"10.3390\/info12080291","type":"journal-article","created":{"date-parts":[[2021,7,22]],"date-time":"2021-07-22T22:35:31Z","timestamp":1626993331000},"page":"291","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Hybrid MultiLayer Perceptron Under-Sampling with Bagging Dealing with a Real-Life Imbalanced Rice Dataset"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5292-2902","authenticated-orcid":false,"given":"Moussa","family":"Diallo","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Wuhan University of Technology, Wuhan 430070, China"},{"name":"Department of Mathematics and Computer Science, Ecole Normale Superieure, 241 Bamako, Mali"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shengwu","family":"Xiong","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Wuhan University of Technology, Wuhan 430070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2260-3526","authenticated-orcid":false,"given":"Eshete Derb","family":"Emiru","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Wuhan University of Technology, Wuhan 430070, China"},{"name":"School of computing, Debre Markos University, DebreMarkos 269, Ethiopia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7127-6256","authenticated-orcid":false,"given":"Awet","family":"Fesseha","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Wuhan University of Technology, Wuhan 430070, China"},{"name":"School of Natural Science and computing, Mekele University, Mekelle 231, Ethiopia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aminu Onimisi","family":"Abdulsalami","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Wuhan University of Technology, Wuhan 430070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohamed Abd","family":"Elaziz","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"105031","DOI":"10.1016\/j.compag.2019.105031","article-title":"Using boosted tree regression and artificial neural networks to forecast upland rice yield under climate change in Sahel","volume":"166","author":"Zhang","year":"2019","journal-title":"Comput. 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