{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T18:57:14Z","timestamp":1769281034175,"version":"3.49.0"},"reference-count":36,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,4,21]],"date-time":"2022-04-21T00:00:00Z","timestamp":1650499200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018YFB0704400"],"award-info":[{"award-number":["2018YFB0704400"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Program of Science and Technology of Yunnan Province","award":["202002AB080001-2"],"award-info":[{"award-number":["202002AB080001-2"]}]},{"name":"Key Research Project of Zhejiang Laboratory","award":["2021PE0AC02"],"award-info":[{"award-number":["2021PE0AC02"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Classification is among the core tasks in machine learning. Existing classification algorithms are typically based on the assumption of at least roughly balanced data classes. When performing tasks involving imbalanced data, such classifiers ignore the minority data in consideration of the overall accuracy. The performance of traditional classification algorithms based on the assumption of balanced data distribution is insufficient because the minority-class samples are often more important than others, such as positive samples, in disease diagnosis. In this study, we propose a cost-sensitive variational autoencoding classifier that combines data-level and algorithm-level methods to solve the problem of imbalanced data classification. Cost-sensitive factors are introduced to assign a high cost to the misclassification of minority data, which biases the classifier toward minority data. We also designed misclassification costs closely related to tasks by embedding domain knowledge. Experimental results show that the proposed method performed the classification of bulk amorphous materials well.<\/jats:p>","DOI":"10.3390\/a15050139","type":"journal-article","created":{"date-parts":[[2022,4,22]],"date-time":"2022-04-22T05:07:22Z","timestamp":1650604042000},"page":"139","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Cost-Sensitive Variational Autoencoding Classifier for Imbalanced Data Classification"],"prefix":"10.3390","volume":"15","author":[{"given":"Fen","family":"Liu","sequence":"first","affiliation":[{"name":"School of Computer Engineering & Science, Shanghai University, Shanghai 200444, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Quan","family":"Qian","sequence":"additional","affiliation":[{"name":"School of Computer Engineering & Science, Shanghai University, Shanghai 200444, China"},{"name":"Materials Genome Institute, Shanghai University, Shanghai 200444, China"},{"name":"Zhejiang Laboratory, Hangzhou 311100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10115-007-0114-2","article-title":"Top 10 algorithms in data mining","volume":"14","author":"Wu","year":"2008","journal-title":"Knowl. 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