Ad-RuLer: A Novel Rule-Driven Data Synthesis Technique for Imbalanced Classification

Ad-RuLer: A Novel Rule-Driven Data Synthesis Technique for Imbalanced Classification

When classifiers face imbalanced class distributions, they often misclassify minority class samples, consequently diminishing the predictive performance of machine learning models. Existing oversampling techniques predominantly rely on the selection of neighboring data via interpolation, with less e...

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Bibliographic Details
Main Authors:	Xiao Zhang, Iván Paz, Àngela Nebot, Francisco Mugica, Enrique Romero
Format:	Article
Language:	English
Published:	MDPI AG 2023-11-01
Series:	Applied Sciences
Subjects:	rule-based approach oversampling data synthesis imbalanced data classification
Online Access:	https://www.mdpi.com/2076-3417/13/23/12636

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