Abstract
A process for generating a unifying motif-based homogeneous feature vector representation is described and evaluated. The motivation was to determine the viability of this representation as a unifying representation for heterogeneous data classification. The focus for the work was cardiovascular disease classification. The reported evaluation indicates that the proposed unifying representation is a viable one, producing better classification results than when a Recurrent Neural Network (RNNs) was applied to just ECG time series data.
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Aldosari, H., Coenen, F., Lip, G.Y.H., Zheng, Y. (2021). Motif Based Feature Vectors: Towards a Homogeneous Data Representation for Cardiovascular Diseases Classification. In: Golfarelli, M., Wrembel, R., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2021. Lecture Notes in Computer Science(), vol 12925. Springer, Cham. https://doi.org/10.1007/978-3-030-86534-4_22
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DOI: https://doi.org/10.1007/978-3-030-86534-4_22
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