{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,2]],"date-time":"2025-10-02T06:15:52Z","timestamp":1759385752314},"reference-count":64,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2020,3,6]],"date-time":"2020-03-06T00:00:00Z","timestamp":1583452800000},"content-version":"vor","delay-in-days":1052,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,10,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Classification tasks are an integral part of science, industry, business, and health care systems; being such a pervasive technique, its smallest improvement is valuable. Artificial Neural Network (ANN) is one of the strongest techniques used in many disciplines for classification. The ANN technique suffers from drawbacks such as intransparency in spite of its high prediction power. In this paper, motivated by learning styles in human brains, ANN's shortcomings are assuaged and its prediction power is improved. Self-Organizing Map (SOM), an ANN variation which has strong unsupervised power, and Feedforward ANN, traditionally used for classification tasks, are hybridized to solidify their benefits and help remove their limitations. The proposed method, which we name Self-Organizing Error-Driven (SOED) Artificial Neural Network, shows significant improvements in comparison with usual ANNs. We show SOED is a more accurate, more reliable, and more transparent technique through experimentation with five different datasets.<\/jats:p>\n               <jats:p>Highlights A synthesis of MLP and SOM is presented for tackling classification challenges. The superiority of SOED over MLP in addressing 5 classification tasks is presented. SOED is compared with other states of the art techniques such as DT, KNN, and SVM. It is shown that SOED is a more accurate and reliable in comparison with MLP. It is shown SOED is more accurate, reliable and transparent in comparison with MLP.<\/jats:p>","DOI":"10.1016\/j.jcde.2017.04.003","type":"journal-article","created":{"date-parts":[[2017,4,19]],"date-time":"2017-04-19T22:17:51Z","timestamp":1492640271000},"page":"282-304","source":"Crossref","is-referenced-by-count":9,"title":["Self-Organizing and Error Driven (SOED) artificial neural network for smarter classifications"],"prefix":"10.1093","volume":"4","author":[{"given":"Ruholla","family":"Jafari-Marandi","sequence":"first","affiliation":[{"name":"Department of Industrial and Systems Engineering, Mississippi State University, 260 McCain Engineering Building, MS 39762, United States"}]},{"given":"Mojtaba","family":"Khanzadeh","sequence":"first","affiliation":[{"name":"Department of Industrial and Systems Engineering, Mississippi State University, 260 McCain Engineering Building, MS 39762, United States"}]},{"given":"Brian K.","family":"Smith","sequence":"first","affiliation":[{"name":"Department of Industrial and Systems Engineering, Mississippi State University, 260 McCain Engineering Building, MS 39762, United States"}]},{"given":"Linkan","family":"Bian","sequence":"first","affiliation":[{"name":"Department of Industrial and Systems Engineering, Mississippi State University, 260 McCain Engineering Building, MS 39762, United States"}]}],"member":"286","published-online":{"date-parts":[[2017,4,19]]},"reference":[{"key":"2020042823261631000_b0005","doi-asserted-by":"crossref","DOI":"10.1002\/0471497398.mm421","article-title":"Artificial neural networks","volume-title":"Handbook of Measuring System Design","author":"Abraham","year":"2005"},{"key":"2020042823261631000_b0010","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1016\/S0893-6080(03)00116-3","article-title":"A generalized feedforward neural network architecture for classification and regression","volume":"16","author":"Arulampalam","year":"2003","journal-title":"Neural Networks"},{"key":"2020042823261631000_b0015","volume-title":"UCI machine learning repository","author":"Asuncion","year":"2007"},{"key":"2020042823261631000_b0020","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.knosys.2012.06.014","article-title":"Optimum estimation of missing values in randomized complete block design by genetic algorithm","volume":"37","author":"Azadeh","year":"2013","journal-title":"Knowledge-Based Systems"},{"key":"2020042823261631000_b0025","first-page":"1","article-title":"Special issue on data analysis and classification in marketing\u2014preface by the guest editors","volume-title":"Advances in Data Analysis and Classification","author":"Baier","year":"2012"},{"key":"2020042823261631000_b0030","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/S0377-2217(99)00192-7","article-title":"Multicriteria assignment method PROAFTN: Methodology and medical application","volume":"125","author":"Belacel","year":"2000","journal-title":"European Journal of Operational Research"},{"key":"2020042823261631000_b0035","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.jcde.2016.06.002","article-title":"Data-mining modeling for the prediction of wear on forming-taps in the threading of steel components","volume":"3","author":"Bustillo","year":"2016","journal-title":"Journal of Computational Design and Engineering"},{"key":"2020042823261631000_b0040","first-page":"321","article-title":"A review of classification","volume-title":"Journal of the Royal Statistical Society. 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