Clustering and Nearest Neighbour Based Classification Approach for Mobile Activity Recognition

Bashir, S. and Doolan, Daniel and Petrovski, Andrei (2016) Clustering and Nearest Neighbour Based Classification Approach for Mobile Activity Recognition. Journal of Mobile Multimedia, 12 (1&2). pp. 110-124. ISSN 1937-9412

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Abstract

We present a hybridized algorithm based on clustering and nearest neighbour classifier for mobile activity recognition. The algorithm transforms a training dataset into a more compact and reduced representative set that lessens the computational cost on mobile devices. This is achieved by applying clustering on the original dataset with the concept of percentage data retention to direct the operation. After clustering, we extract three reduced and transformed representation of the original dataset to serve as the reference data for nearest neighbour classification. These reduced representative sets can be used for classifying new instances using the nearest neighbour algorithm step on the mobile phone. Experimental evaluation of our proposed approach using real mobile activity recognition dataset shows improved result over the basic KNN algorithm that uses all the training dataset.

Item Type: Article
Subjects: G400 Computer Science
Divisions: Faculty of Computing, Engineering and the Built Environment
Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology > Cyber Security
UoA Collections > UoA11: Computer Science and Informatics
Depositing User: $ Ian McDonald
Date Deposited: 03 Mar 2017 15:34
Last Modified: 30 Nov 2017 11:04
URI: http://www.open-access.bcu.ac.uk/id/eprint/3966

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