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Online semi-supervised multi-channel time series classifier based on growing neural gas

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Abstract

Challenges in time series classification has attracted attention in the past decade. Although large amounts of labeled data are assumed to be available, in reality, labeled data might be scarce to find in many domains. In this paper, we propose an online semi-supervised multi-channel classifier for time series based on growing neural gas (GNG) learning scheme. The method is able to handle multi-channel time series with variation in dimensions and it introduces a label prediction strategy to minimize misclassification. It measures the similarity of input instance and learned templates using weighted multi-channel dynamic time warping technique and learns the topology of input data space specified for each class using the GNG learning algorithm. Comprehensive evaluation is conducted using various datasets, such as gesture recognition, human activity recognition, and human daily-life activity recognition. Experimental results demonstrate good classification results, with indication that the proposed approach requires only a handful of labeled instances to construct an accurate classification model.

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Acknowledgments

The project is funded under the University of Malaya Grand Challenge Grant (GC003A-14HTM).

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Authors and Affiliations

  1. Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia

    Parham Nooralishahi & Chu Kiong Loo

  2. Faculty of Engineering, Computing and Science, Swinburne University of Technology (Sarawak Campus), Kuching, Sarawak, Malaysia

    Manjeevan Seera

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  1. Parham Nooralishahi
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  2. Manjeevan Seera
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Correspondence to Manjeevan Seera.

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Nooralishahi, P., Seera, M. & Loo, C.K. Online semi-supervised multi-channel time series classifier based on growing neural gas. Neural Comput & Applic 28, 3491–3505 (2017). https://doi.org/10.1007/s00521-016-2247-2

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