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作者机构:Department of Bioresource Engineering McGill University 21 111 Lakeshore Road Ste-Anne-de-BellevueQCH9X 3V9 Canada Process Quality Engineering School of Workforce Development Conestoga College Institute of Technology and Advanced Learning 299 Doon Valley Drive KitchenerONN2G 4M4 Canada
出 版 物:《SSRN》
年 卷 期:2024年
核心收录:
摘 要:Imbalanced data situation exists in most fields of endeavours. The problem has been identified as a major bottleneck in machine learning/data mining and is becoming a serious issue of concern in the Food processing applications. As a result of rare cases occurring infrequently, classification rules. that detect small groups are scarce, so samples belonging to small classes are largely misclassified. Most existing machine learning algorithms including the K-means, decision trees, and support vector machine (SVM) are not optimal in handling imbalanced data. Consequently, models developed from analysis of such data are very prone to rejection and non-adoptability in the real industrial and commercial settings. This paper showcased the reality of imbalance data problem in agro-food applications and proposes possible ways of handling the problem using methods including one-class learning, ensemble methods, data resampling, and feature selection techniques. Rightly analysing imbalanced data from food processing application research works will improve accuracy of results and model developments. This will consequently enhance acceptability and adoptability of innovations/inventions. © 2024, The Authors. All rights reserved.