Improving performance of decision threshold moving-based strategies by integrating density-based clustering technique

Improving performance of decision threshold moving-based strategies by integrating density-based clustering technique

Class imbalance learning (CIL), which aims to addressing the performance degradation problem of traditional supervised learning algorithms in the scenarios of skewed data distribution, has become one of research hotspots in fields of machine learning, data mining, and artificial intelligence. As a p...

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Bibliographic Details
Main Authors: Mengke Lu, Shang Gao, Xibei Yang, Hualong Yu
Format: Article
Language:English
Published: AIMS Press 2023-03-01
Series:Electronic Research Archive
Subjects:
class imbalance learning
decision threshold moving
clustering
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Online Access:https://www.aimspress.com/article/doi/10.3934/era.2023127?viewType=HTML

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https://www.aimspress.com/article/doi/10.3934/era.2023127?viewType=HTML

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