A new classification strategy for human activity recognition using cost sensitive support vector machines for imbalanced data

Bilal M’hamed Abidine (Faculty of Electronics and Computer Sciences, University of Sciences and Technology Houari Boumediene (USTHB), Algiers, Algeria)
Belkacem Fergani (Faculty of Electronics and Computer Sciences, University of Sciences and Technology Houari Boumediene (USTHB), Algiers, Algeria)
Mourad Oussalah (School of Electronics, Electrical and Computer Engineering, University of Birmingham, Birmingham, UK)
Lamya Fergani (Faculty of Electronics and Computer Sciences, University of Sciences and Technology Houari Boumediene (USTHB), Algiers, Algeria)

Kybernetes

ISSN: 0368-492X

Publication date: 26 August 2014

Abstract

Purpose

The task of identifying activity classes from sensor information in smart home is very challenging because of the imbalanced nature of such data set where some activities occur more frequently than others. Typically probabilistic models such as Hidden Markov Model (HMM) and Conditional Random Fields (CRF) are known as commonly employed for such purpose. The paper aims to discuss these issues.

Design/methodology/approach

In this work, the authors propose a robust strategy combining the Synthetic Minority Over-sampling Technique (SMOTE) with Cost Sensitive Support Vector Machines (CS-SVM) with an adaptive tuning of cost parameter in order to handle imbalanced data problem.

Findings

The results have demonstrated the usefulness of the approach through comparison with state of art of approaches including HMM, CRF, the traditional C-Support vector machines (C-SVM) and the Cost-Sensitive-SVM (CS-SVM) for classifying the activities using binary and ubiquitous sensors.

Originality/value

Performance metrics in the experiment/simulation include Accuracy, Precision/Recall and F measure.

Keywords

Citation

M’hamed Abidine, B., Fergani, B., Oussalah, M. and Fergani, L. (2014), "A new classification strategy for human activity recognition using cost sensitive support vector machines for imbalanced data", Kybernetes, Vol. 43 No. 8, pp. 1150-1164. https://doi.org/10.1108/K-07-2014-0138

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Publisher

:

Emerald Group Publishing Limited

Copyright © 2014, Emerald Group Publishing Limited

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