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Towards an efficient one-class classifier for mobile devices and wearable sensors on the context of personal risk detection

Trejo, Luis A.; Barrera-Animas, Ari Yair

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Authors

Luis A. Trejo

Ari Yair Barrera-Animas



Abstract

In this work, we present a first step towards an efficient one-class classifier well suited for mobile devices to be implemented as part of a user application coupled with wearable sensors in the context of personal risk detection. We compared one-class Support Vector Machine (ocSVM) and OCKRA (One-Class K-means with Randomly-projected features Algorithm). Both classifiers were tested using four versions of the publicly available PRIDE (Personal RIsk DEtection) dataset. The first version is the original PRIDE dataset, which is based only on time-domain features. We created a second version that is simply an extension of the original dataset with new attributes in the frequency domain. The other two datasets are a subset of these two versions, after a feature selection procedure based on a correlation matrix analysis followed by a Principal Component Analysis. All experiments were focused on the performance of the classifiers as well as on the execution time during the training and classification processes. Therefore, our goal in this work is twofold: we aim at reducing execution time but at the same time maintaining a good classification performance. Our results show that OCKRA achieved on average, 89.1% of Area Under the Curve (AUC) using the full set of features and 83.7% when trained using a subset of them. Furthermore, regarding execution time, OCKRA reports in the best case a 33.1% gain when using a subset of the feature vector, instead of the full set of features. These results are better than those reported by ocSVM, in which case, even though the AUCs are very close to each other, execution times are significantly higher in all cases, for example, more than 20 h versus less than an hour in the worst-case scenario. Having in mind the trade-off between classification performance and efficiency, our results support the choice of OCKRA as our best candidate so far for a mobile implementation where less processing and memory resources are at hand. OCKRA reports a very encouraging speed-up without sacrificing the classifier performance when using the PRIDE dataset based only on time-domain attributes after a feature selection procedure.

Citation

Trejo, L. A., & Barrera-Animas, A. Y. (2018). Towards an efficient one-class classifier for mobile devices and wearable sensors on the context of personal risk detection. Sensors, 18(9), https://doi.org/10.3390/s18092857

Journal Article Type Article
Acceptance Date Aug 23, 2018
Online Publication Date Aug 30, 2018
Publication Date Sep 1, 2018
Deposit Date Feb 18, 2020
Publicly Available Date Feb 19, 2020
Journal Sensors
Print ISSN 1424-8220
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 18
Issue 9
Article Number 2857
DOI https://doi.org/10.3390/s18092857
Keywords time-domain features, frequency-domain features, principal component analysis, behaviour analysis, classifier efficiency, personal risk detection, one-class classification, wearable sensors
Public URL https://uwe-repository.worktribe.com/output/5130097

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