A One-Vs-One classifier ensemble with majority voting for activity recognition

Romera-Paredes, B., Aung, M. S.H. and Bianchi-Berthouze, N. (2013) A One-Vs-One classifier ensemble with majority voting for activity recognition. In: ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning . UNSPECIFIED, BEL, pp. 443-448. ISBN 9782874190810

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Abstract

A solution for the automated recognition of six full body motion activities is proposed. This problem is posed by the release of the Activity Recognition database [1] and forms the basis for a classification competition at the European Symposium on Artificial Neural Networks 2013. The data-set consists of motion characteristics of thirty subjects captured using a single device delivering accelerometric and gyroscopic data. Included in the released data-set are 561 processed features in both the time and frequency domains. The proposed recognition framework consists of an ensemble of linear support vector machines each trained to discriminate a single motion activity against another single activity. A majority voting rule is used to determine the final outcome. For comparison, a six "winner take all" multiclass support vector machine ensemble and k-Nearest Neighbour models were also implemented. Results show that the system accuracy for the one versus one ensemble is 96.4% for the competition test set. Similarly, the multiclass SVM ensemble and k-Nearest Neighbour returned accuracies of 93.7% and 90.6% respectively. The outcomes of the one versus one method were submitted to the competition resulting in the winning solution.

Item Type: Book Section
Uncontrolled Keywords: artificial intelligence,information systems ,/dk/atira/pure/subjectarea/asjc/1700/1702
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Depositing User: LivePure Connector
Date Deposited: 27 Nov 2019 02:17
Last Modified: 22 Apr 2020 11:24
URI: https://ueaeprints.uea.ac.uk/id/eprint/73161
DOI:

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