Abstract
While the field of classification is witnessing excellent achievement in recent years, not much attention is given to methods that deal with the time series data. In this paper, we propose a modular system for the classification of time series data. The proposed approach explores the diversity through various input representation techniques, each of which focuses on a certain aspect of the temporal patterns. The temporal patterns are identified by aggregation of the decisions of multiple classifiers trained through different representations of the input data. Several time series data sets are employed to examine the validity of the proposed approach. The results obtained from our experiments show that the performance of the proposed approach is effective as well as robust.
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Chen, L., Kamel, M., Jiang, J. (2004). A Modular System for the Classification of Time Series Data. In: Roli, F., Kittler, J., Windeatt, T. (eds) Multiple Classifier Systems. MCS 2004. Lecture Notes in Computer Science, vol 3077. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25966-4_13
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DOI: https://doi.org/10.1007/978-3-540-25966-4_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22144-9
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