Evo-TFS：基于进化时间-频率域的合成少数类过采样方法用于不平衡时间序列分类
Evo-TFS: Evolutionary Time-Frequency Domain-Based Synthetic Minority Oversampling Approach to Imbalanced Time Series Classification
AI总结：
Evo-TFS通过整合时间和频率域特征，提出了一种进化过采样方法，有效提升不平衡时间序列分类性能。
摘要：
时间序列分类是机器学习中的基本任务，具有广泛的实际应用。尽管许多深度学习方法在学习时间序列数据用于分类方面已被证明有效，但它们最初是在平衡数据分布的假设下开发的。一旦数据分布不均，这些方法往往忽视少数类，而少数类通常具有更高的实际意义。过采样方法已被设计用来解决这个问题，通过生成少数类样本，但其依赖于线性插值往往阻碍了时间动态的保持和多样样本的生成。因此，在本文中，我们提出了Evo-TFS，一种新的进化过采样方法，整合了时间和频率域的特征。在Evo-TFS中，使用强类型遗传编程来进化多样化、高质量的时间序列，由结合时间和频率域特征的适应度函数指导。在不平衡时间序列数据集上的实验表明，Evo-TFS优于现有过采样方法，显著提高了时间和频率域分类器的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
