Contrastive Representation-Guided Genetic Minority Oversampling for Imbalanced Time-Series Classification

2026-08-24

AI Summary

为解决不平衡时间序列分类问题，提出了一种基于对比表示引导的遗传少数类过采样方法FreMGP，通过频率域表征指导生成高质量合成样本。

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.

Problem

• imbalanced time-series classification
• sampling methods
• minority-class samples

Innovation

• Frequency-domain representation
• Contrastive learning
• Genetic Programming
• Oversampling
• Imbalanced time-series classification