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...

وصف كامل

التفاصيل البيبلوغرافية
الحاوية / القاعدة:	JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
المؤلفون الرئيسيون:	Wang, Yujiang; Rosli, Marshima Mohd; Musa, Norzilah; Wang, Lei
التنسيق:	مقال
اللغة:	English
منشور في:	SPRINGERNATURE 2024
الموضوعات:	Computer Science
الوصول للمادة أونلاين:	https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001410486900001

مواد مشابهة

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