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This research addresses the critical challenge of class imbalance in time-series classification by introducing FreMGP, a method that combines frequency-domain contrastive learning with genetic programming to generate high-quality synthetic minority samples. The approach significantly improves model generalization and diversity compared to existing interpolation or deep generative techniques, benefiting both traditional machine learning and deep learning classifiers. By guiding evolutionary search toward discriminative representations, this work offers a robust pre-processing solution for real-world applications where data scarcity skews predictive outcomes.
Contrastive Representation-Guided Genetic Minority Oversampling for Imbalanced Time-Series Classification
arXiv:2608.22804v1 Announce Type: new Abstract: Real-world time-series classification tasks often exhibit class imbalance, which can be extremely severe in some applications. To avoid training biased classifiers on imbalanced data, sampling is one of the most popular data pre-processing techniques because of its classifier-agnostic nature. However, due to the complex temporal dependencies in original time-series data and the scarcity of minority-class samples, existing sampling methods, including interpolation-based oversampling methods and deep learning-based generative models, usually suffer from limited generalization and poor diversity when generating new time-series samples. This paper proposes a Frequency-domain representation-guided Multi-tree Genetic Programming-based oversampling approach (FreMGP) to imbalanced time-series classification, where each individual represents a set of synthetic samples for the minority class. A frequency-domain class-discriminative representation module based on contrastive learning is also developed, guiding the evolutionary search toward high-quality synthetic time-series samples. Experiments on imbalanced time-series datasets demonstrate that FreMGP outperforms existing oversampling methods and consistently improves the performance of different classifiers, including both general machine learning and deep learning models.
Abstract:Real-world time-series classification tasks often exhibit class imbalance, which can be extremely severe in some applications. To avoid training biased classifiers on imbalanced data, sampling is one of the most popular data pre-processing techniques because of its classifier-agnostic nature. However, due to the complex temporal dependencies in original time-series data and the scarcity of minority-class samples, existing sampling methods, including interpolation-based oversampling methods and deep learning-based generative models, usually suffer from limited generalization and poor diversity when generating new time-series samples. This paper proposes a Frequency-domain representation-guided Multi-tree Genetic Programming-based oversampling approach (FreMGP) to imbalanced time-series classification, where each individual represents a set of synthetic samples for the minority class. A frequency-domain class-discriminative representation module based on contrastive learning is also developed, guiding the evolutionary search toward high-quality synthetic time-series samples. Experiments on imbalanced time-series datasets demonstrate that FreMGP outperforms existing oversampling methods and consistently improves the performance of different classifiers, including both general machine learning and deep learning models.
Machine Learning (cs.LG)
arXiv:2608.22804 [cs.LG]
(or arXiv:2608.22804v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2608.22804 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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