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International Journal of Intelligent Data and Machine Learning

Open Access Peer Review International
Open Access

An Interpretable Visual Analytics Framework for Machine Learning–Based Multichannel Time Series Classification and Performance Evaluation

DOI https://doi.org/10.55640/ijidml-v03i08-01
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Dr. Kwame Mensah 1 , Dr. Abena Owusu 1 ,
4 Department of Computer Science and Artificial Intelligence East China Institute of Intelligent Computing Shanghai, China
4 School of Artificial Intelligence and Data Science Ghana Advanced Computing University Kumasi, Ghana

Abstract

Multichannel time series classification (MCTSC) has emerged as a critical research area in machine learning due to its wide applicability in healthcare monitoring, human activity recognition, and industrial signal analysis. Despite significant advances in deep learning and hybrid architectures, the interpretability of classification outcomes and the transparency of model evaluation remain major challenges. This paper proposes a conceptual and analytical visual analytics framework designed to enhance interpretability in machine learning–based multichannel time series classification systems. The framework integrates predictive modeling, feature representation learning, and visual performance diagnostics to enable comprehensive understanding of both model behavior and data dynamics. Building upon benchmark datasets and established methodologies in time series classification research (Bagnall et al., 2017; Bagnall et al., 2018), the study systematically evaluates how visual analytics can bridge the gap between model accuracy and interpretability. The framework also incorporates insights from spectral classification approaches and machine learning pipelines used in multivariate signal environments (Acuna et al., 2024). Experimental and comparative analysis suggests that combining visual interpretability layers with classification models improves diagnostic transparency, facilitates error analysis, and enhances decision confidence in real-world applications. The findings highlight the importance of integrating visualization-driven interpretability into next-generation time series classification systems.

Keywords

Multichannel Time Series, Machine Learning, Visual Analytics, Interpretability

References

Acuna, E., Kendziora, C., Fustenberg, R., Breshike, C.J. and Kendziora, D. (2024). Machine Learning Algorithmsfor Analytes Classification Based on Simulated Spectra. Proceedings of SPIE 13031, Algorithms, Technologies,and Applications for Multispectral and Hyperspectral Imaging XXX, 130310H. https://doi.org/10.1117/12.3013758
Bagnall, A., Dau, H.A., Lines, J., Flynn, M., Large, J., Bostrom, A., Southam, P. and Keogh, E. (2018). The UEAMultivariate Time Series Classification Archive. arXiv Preprint arXiv:1811.00075. https://doi.org/10.48550/arXiv.1811.00075
Bagnall, A., Lines, J., Bostrom, A., Large, J. and Keogh, E. (2017). The Great Time Series Classification Bake Off:A Review and Experimental Evaluation of Recent Algorithmic Advances. Data Mining and KnowledgeDiscovery, 31(3), 606-660. https://doi.org/10.1007/s10618-016-0483-9
Baldan, F. and Benítez, J. (2020). Multivariable Time Series Classification through an InterpretableRepresentation. arXiv Preprint arXiv:2009.03614. https://doi.org/10.48550/arXiv.2009.03614
Birbaumer, N., Ghanayim, N., Hinterberger, T., Iversen, I., Kotchoubey, B., Kubler, A., Perelmouter, J., Taub, E.and Flor, H. (1999). A Spelling Device for the Paralysed. Nature, 398(6725), 297. https://doi.org/10.1038/18581
Blankertz, B., Curio, G. and Müller, K.R. (2002). Classifying Single Trial EEG: Towards Brain ComputerInterfacing. In Advances in Neural Information Processing Systems, 157-164.
Cura, A., Kucuk, H., Ergen, E. and Oksuzoglu, I.B. (2020). Driver Profiling Using Long Short Term Memory(LSTM) and Convolutional Neural Network (CNN) Methods. IEEE Transactions on Intelligent TransportationSystems, 1-11. https://doi.org/10.1109/TITS.2020.2995722
Cuturi, M. (2011). Fast Global Alignment Kernels. In Proceedings of the 28th International Conference on MachineLearning (ICML-11), 929-936.

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