Clustering and nearest neighbour based classification approach for mobile activity recognition.
Bashir, S.A; Doolan, D.C.; Petrovski, A.
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.
BASHIR, S.A., DOOLAN, D.C. and PETROVSKI, A. 2016. Clustering and nearest neighbour based classification approach for mobile activity recognition. Journal of mobile multimedia [online], 12(1-2), pages 100-124. Available from: http://www.rintonpress.com/xjmm12/jmm-12-12/110-124.pdf
|Journal Article Type||Article|
|Acceptance Date||Apr 3, 2016|
|Online Publication Date||Apr 3, 2016|
|Publication Date||Apr 8, 2016|
|Deposit Date||Oct 4, 2016|
|Publicly Available Date||Oct 4, 2016|
|Journal||Journal of mobile multimedia|
|Peer Reviewed||Peer Reviewed|
|Keywords||Activity recognition; KNN; Smartphones; Clustering|
BASHIR 2016 Clustering and nearest neighbour based
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