Oversampling With Reliably Expanding Minority Class Regions for Imbalanced Data Learning
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Knowledge and Data Engineering"
DOI: 10.1109/tkde.2022.3171706
Abstract: This paper proposes a simple interpolation Oversampling method with the purpose of Reliably Expanding the Minority class regions (OREM). OREM first finds the candidate minority region around each original minority sample, then exploits this region… read more here.
Keywords: class; expanding minority; class regions; minority class ... See more keywords
Embedding Undersampling Rotation Forest for Imbalanced Problem
Sign Up to like & getrecommendations! Published in 2018 at "Computational Intelligence and Neuroscience"
DOI: 10.1155/2018/6798042
Abstract: Rotation Forest is an ensemble learning approach achieving better performance comparing to Bagging and Boosting through building accurate and diverse classifiers using rotated feature space. However, like other conventional classifiers, Rotation Forest does not work… read more here.
Keywords: undersampling rotation; class; rotation forest; embedding undersampling ... See more keywords
A Partition-Based Hybrid Algorithm for Effective Imbalanced Classification
Sign Up to like & getrecommendations! Published in 2025 at "Data"
DOI: 10.3390/data10040054
Abstract: Imbalanced classification presents a significant challenge in real-world datasets, requiring innovative solutions to enhance performance. This study introduces a hybrid binary classification algorithm designed to effectively address this challenge. The algorithm identifies different data types,… read more here.
Keywords: classification; minority class; performance; imbalanced classification ... See more keywords
Improving Minority Class Recall through a Novel Cluster-Based Oversampling Technique
Sign Up to like & getrecommendations! Published in 2024 at "Informatics"
DOI: 10.3390/informatics11020035
Abstract: In this study, we propose an approach to address the pressing issue of false negative errors by enhancing minority class recall within imbalanced data sets commonly encountered in machine learning applications. Through the utilization of… read more here.
Keywords: cluster based; class recall; minority class;