Empirical Comparison of EEG Signal Classification Techniques through Genetic Programming-based AutoML: An Extended Study





AutoML, Classification, EEG, End-to-end Machine Learning, Genetic Programming, Sleep Spindles


Machine Learning (ML) applications using complex data often need multiple preprocessing techniques and predictive models to find a solution that meets their needs. In this context, Automated Machine Learning (AutoML) techniques help to provide automated data preparation and modeling and improve ML pipelines. AutoML can follow different strategies, among them Genetic Programming (GP). GP stands out for its ability to create pipelines of arbitrary format, with high interpretability and the ability to customize information from the data domain context. This paper presents a comparative study of two AutoML approaches optimized with GP for the time series classification problem and its characterization through four domain-based feature sets. We selected the Electroencephalogram (EEG) signals as a case of study due to their high complexity, spatial and temporal co-variance, and non-stationarity. Our data characterization shows that using only spectral or time-domain features is unsuitable for achieving high-performance pipelines. Our results reveal how AutoML can generate more accurate and interpretable solutions than the literature's complex or ad hoc models. The proposed approach facilitates the analysis of dimensional reduction through fitness convergence, tree depth, and generated features.


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How to Cite

M. Miranda, I., de C. Aranha, C., C. P. L. F. de Carvalho, A., & P. F. Garcia, L. (2024). Empirical Comparison of EEG Signal Classification Techniques through Genetic Programming-based AutoML: An Extended Study. Journal of Information and Data Management, 15(1), 175–185. https://doi.org/10.5753/jidm.2024.3369



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