{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T17:26:07Z","timestamp":1754155567476,"version":"3.41.2"},"reference-count":29,"publisher":"Emerald","issue":"8","license":[{"start":{"date-parts":[[2014,8,26]],"date-time":"2014-08-26T00:00:00Z","timestamp":1409011200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2014,8,26]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-heading\">Purpose<\/jats:title><jats:p>\u2013 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.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Design\/methodology\/approach<\/jats:title><jats:p>\u2013 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.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Findings<\/jats:title><jats:p>\u2013 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.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Originality\/value<\/jats:title><jats:p>\u2013 Performance metrics in the experiment\/simulation include Accuracy, Precision\/Recall and F measure.<\/jats:p><\/jats:sec>","DOI":"10.1108\/k-07-2014-0138","type":"journal-article","created":{"date-parts":[[2014,10,3]],"date-time":"2014-10-03T11:46:56Z","timestamp":1412336816000},"page":"1150-1164","source":"Crossref","is-referenced-by-count":14,"title":["A new classification strategy for human activity recognition using cost sensitive support vector machines for imbalanced data"],"prefix":"10.1108","volume":"43","author":[{"given":"Bilal","family":"M\u2019hamed Abidine","sequence":"first","affiliation":[]},{"given":"Belkacem","family":"Fergani","sequence":"additional","affiliation":[]},{"given":"Mourad","family":"Oussalah","sequence":"additional","affiliation":[]},{"given":"Lamya","family":"Fergani","sequence":"additional","affiliation":[]}],"member":"140","reference":[{"key":"key2020123000001740600_b1","doi-asserted-by":"crossref","unstructured":"Abidine, M. , Fergani, B. and Clavier, L. 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