发表机构
Dalian University of Technology; Nanyang Technological University（大连理工大学; 南洋理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对不平衡时间序列分类中现有过采样方法泛化与多样性不足的问题，提出FreMGP方法，结合对比学习的频域判别表示引导多树遗传规划，实验证明其性能优于现有方法并提升各类分类器表现。
AI 中文摘要
现实世界中的时间序列分类任务常存在类别不平衡问题，在部分应用中不平衡程度极为严重。为避免在不平衡数据上训练出有偏分类器，采样是最常用的数据预处理技术之一，因其与分类器无关。但由于原始时间序列数据中存在复杂的时间依赖关系，且少数类样本稀缺，现有采样方法（包括基于插值的过采样方法和基于深度学习的生成模型）在生成新时间序列样本时通常存在泛化能力有限、多样性差的问题。本文提出一种频域表示引导的基于多树遗传规划的过采样方法（FreMGP），用于不平衡时间序列分类，其中每个个体代表少数类的一组合成样本。本文还开发了一种基于对比学习的频域类别判别表示模块，引导进化搜索生成高质量的合成时间序列样本。在不平衡时间序列数据集上的实验表明，FreMGP优于现有过采样方法，且能持续提升不同分类器的性能，包括通用机器学习模型和深度学习模型。
英文摘要
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.
