Improving clustering-based and adaptive position-aware interpolation oversampling for imbalanced data classification

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Improving clustering-based and adaptive position-aware interpolation oversampling for imbalanced data classification

Class imbalance is one of the most significant difficulties in modern machine learning. This is because of the inherent bias of standard classifiers toward favoring majority instances while often ignoring minority instances. Interpolation-based oversampling techniques are among the most popular solu...

書誌詳細
出版年:	JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
主要な著者:	Wang, Yujiang; Rosli, Marshima Mohd; Musa, Norzilah; Wang, Lei
フォーマット:	論文
言語:	English
出版事項:	SPRINGERNATURE 2024
主題:	Computer Science
オンライン･アクセス:	https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001410486900001

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