Evo-TFS: Evolutionary Time-Frequency Domain-Based Synthetic Minority Oversampling Approach to Imbalanced Time Series Classification

TL;DR

Evo-TFS is an innovative evolutionary oversampling technique that combines time- and frequency-domain features to improve classification of imbalanced time series data, outperforming existing methods.

Contribution

It introduces a novel genetic programming-based oversampling approach that preserves temporal dynamics and enhances classifier performance on imbalanced datasets.

Findings

Evo-TFS outperforms existing oversampling methods.

It improves classifier accuracy on imbalanced datasets.

The method effectively preserves temporal and frequency features.

Abstract

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.