TY - JOUR AB - 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. VL - 43 IS - 8 SN - 0368-492X DO - 10.1108/K-07-2014-0138 UR - https://doi.org/10.1108/K-07-2014-0138 AU - M’hamed Abidine Bilal AU - Fergani Belkacem AU - Oussalah Mourad AU - Fergani Lamya ED - Dr Mourad Oussalah and Professor Ali Hessami PY - 2014 Y1 - 2014/01/01 TI - A new classification strategy for human activity recognition using cost sensitive support vector machines for imbalanced data T2 - Kybernetes PB - Emerald Group Publishing Limited SP - 1150 EP - 1164 Y2 - 2024/03/29 ER -