Adaptive Oversampling via Density Estimation for Online Imbalanced Classification
Online learning is a framework for processing and learning from sequential data in real time, offering benefits such as promptness and low memory usage. However, it faces critical challenges, including concept drift, where data distributions evolve over time, and class imbalance, which significantly...
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MDPI AG
2025-01-01
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| author | Daeun Lee Hyunjoong Kim |
| author_facet | Daeun Lee Hyunjoong Kim |
| author_sort | Daeun Lee |
| collection | DOAJ |
| description | Online learning is a framework for processing and learning from sequential data in real time, offering benefits such as promptness and low memory usage. However, it faces critical challenges, including concept drift, where data distributions evolve over time, and class imbalance, which significantly hinders the accurate classification of minority classes. Addressing these issues simultaneously remains a challenging research problem. This study introduces a novel algorithm that integrates adaptive weighted kernel density estimation (awKDE) and a conscious biasing mechanism to efficiently manage memory, while enhancing the classification performance. The proposed method dynamically detects the minority class and employs a biasing strategy to prioritize its representation during training. By generating synthetic minority samples using awKDE, the algorithm adaptively balances class distributions, ensuring robustness in evolving environments. Experimental evaluations across synthetic and real-world datasets demonstrated that the proposed method achieved up to a 13.3 times improvement in classification performance over established oversampling methods and up to a 1.66 times better performance over adaptive rebalancing approaches, while requiring significantly less memory. These results underscore the method’s scalability and practicality for real-time online learning applications. |
| format | Article |
| id | doaj-art-b34034dc247e4c43bbc74450b5a204de |
| institution | Kabale University |
| issn | 2078-2489 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-b34034dc247e4c43bbc74450b5a204de2025-01-24T13:35:11ZengMDPI AGInformation2078-24892025-01-011612310.3390/info16010023Adaptive Oversampling via Density Estimation for Online Imbalanced ClassificationDaeun Lee0Hyunjoong Kim1SAP Labs Korea, Seoul 06578, Republic of KoreaDepartment of Statistics and Data Science, Yonsei University, Seoul 03722, Republic of KoreaOnline learning is a framework for processing and learning from sequential data in real time, offering benefits such as promptness and low memory usage. However, it faces critical challenges, including concept drift, where data distributions evolve over time, and class imbalance, which significantly hinders the accurate classification of minority classes. Addressing these issues simultaneously remains a challenging research problem. This study introduces a novel algorithm that integrates adaptive weighted kernel density estimation (awKDE) and a conscious biasing mechanism to efficiently manage memory, while enhancing the classification performance. The proposed method dynamically detects the minority class and employs a biasing strategy to prioritize its representation during training. By generating synthetic minority samples using awKDE, the algorithm adaptively balances class distributions, ensuring robustness in evolving environments. Experimental evaluations across synthetic and real-world datasets demonstrated that the proposed method achieved up to a 13.3 times improvement in classification performance over established oversampling methods and up to a 1.66 times better performance over adaptive rebalancing approaches, while requiring significantly less memory. These results underscore the method’s scalability and practicality for real-time online learning applications.https://www.mdpi.com/2078-2489/16/1/23online learningimbalanced dataconcept driftkernel density estimation |
| spellingShingle | Daeun Lee Hyunjoong Kim Adaptive Oversampling via Density Estimation for Online Imbalanced Classification Information online learning imbalanced data concept drift kernel density estimation |
| title | Adaptive Oversampling via Density Estimation for Online Imbalanced Classification |
| title_full | Adaptive Oversampling via Density Estimation for Online Imbalanced Classification |
| title_fullStr | Adaptive Oversampling via Density Estimation for Online Imbalanced Classification |
| title_full_unstemmed | Adaptive Oversampling via Density Estimation for Online Imbalanced Classification |
| title_short | Adaptive Oversampling via Density Estimation for Online Imbalanced Classification |
| title_sort | adaptive oversampling via density estimation for online imbalanced classification |
| topic | online learning imbalanced data concept drift kernel density estimation |
| url | https://www.mdpi.com/2078-2489/16/1/23 |
| work_keys_str_mv | AT daeunlee adaptiveoversamplingviadensityestimationforonlineimbalancedclassification AT hyunjoongkim adaptiveoversamplingviadensityestimationforonlineimbalancedclassification |