Time series classification is a fundamental machine learning task with broad real-world applications. Although many deep learning methods have proven effective in learning time-series data for classification, they were originally developed under the assumption of balanced data distributions. Once data distribution is uneven, these methods tend to ignore the minority class that is typically of higher practical significance. Oversampling methods have been designed to address this by generating minority-class samples, but their reliance on linear interpolation often hampers the preservation of temporal dynamics and the generation of diverse samples. Therefore, in this paper, we propose Evo-TFS, a novel evolutionary oversampling method that integrates both time- and frequency-domain characteristics. In Evo-TFS, strongly typed genetic programming is employed to evolve diverse, high-quality time series, guided by a fitness function that incorporates both time-domain and frequency-domain characteristics. Experiments conducted on imbalanced time series datasets demonstrate that Evo-TFS outperforms existing oversampling methods, significantly enhancing the performance of time-domain and frequency-domain classifiers.

翻译：时间序列分类是一项基础的机器学习任务，具有广泛的实际应用。尽管许多深度学习方法已被证明能有效学习时间序列数据以进行分类，但这些方法最初是在数据分布平衡的假设下开发的。一旦数据分布不均衡，这些方法往往会忽略通常具有更高实际意义的少数类。过采样方法旨在通过生成少数类样本来解决此问题，但其对线性插值的依赖常常会阻碍时间动态的保持和多样样本的生成。因此，本文提出Evo-TFS，一种新颖的进化过采样方法，它融合了时域和频域特征。在Evo-TFS中，采用强类型遗传编程来进化多样且高质量的时间序列，其进化过程由一个融合了时域和频域特征的适应度函数指导。在不平衡时间序列数据集上进行的实验表明，Evo-TFS优于现有的过采样方法，显著提升了时域和频域分类器的性能。