{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T10:30:28Z","timestamp":1781519428600,"version":"3.54.1"},"reference-count":51,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2021,7,28]],"date-time":"2021-07-28T00:00:00Z","timestamp":1627430400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Intelligenza Artificiale: The international journal of the AIxIA"],"published-print":{"date-parts":[[2021,7,28]]},"abstract":"<jats:p>Mobile phone based activity recognition uses data obtained from embedded sensors to infer user\u2019s physical activities. The traditional approach for activity recognition employs machine learning algorithms to learn from collected labeled data and induce a model. To enhance the accuracy and hence to improve the overall efficiency of the system, the good classifiers can be combined together. Fusion can be done at the feature level and also at the decision level. In this work, we propose a new hybrid classification model Weighted SVM-KNN to perform automatic recognition of activities that combines a Weighted Support Vector Machines (WSVM) to learn a model with a Weighted K-Nearest Neighbors (WKNN), to classify and identify the ongoing activity. The sensory inputs to the classifier are reduced with the Linear Discriminant Analysis (LDA). We demonstrate how to train the hybrid approach in this setting, introduce an adaptive regularization parameter for WSVM approach, and illustrate how our method outperforms the state-of-the-art on a large benchmark datasets.<\/jats:p>","DOI":"10.3233\/ia-200059","type":"journal-article","created":{"date-parts":[[2021,7,30]],"date-time":"2021-07-30T10:54:05Z","timestamp":1627642445000},"page":"1-15","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":5,"title":["Activity recognition from smartphone data using weighted learning methods"],"prefix":"10.1177","volume":"15","author":[{"given":"M\u2019hamed Bilal","family":"Abidine","sequence":"first","affiliation":[{"name":"Laboratoire d\u2019Ing\u00e9nierie des Syst\u00e8mes Intelligents et Communicants, LISIC Lab., Electronics and Computer Sciences Department, University of Science and Technology Houari Boumediene (USTHB), B.P. 32 El Alia, Bab Ezzouar, Algiers, Algeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Belkacem","family":"Fergani","sequence":"additional","affiliation":[{"name":"Laboratoire d\u2019Ing\u00e9nierie des Syst\u00e8mes Intelligents et Communicants, LISIC Lab., Electronics and Computer Sciences Department, University of Science and Technology Houari Boumediene (USTHB), B.P. 32 El Alia, Bab Ezzouar, Algiers, Algeria"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2021,7,28]]},"reference":[{"key":"ref001","first-page":"1","author":"Fahim M.","year":"2013","journal-title":"Applied Intelligence"},{"key":"ref002","author":"Lara O.D.","year":"2012","journal-title":"Proceedings of the IEEE Consumer Communications and Networking Conference (CCNC\u201912)"},{"key":"ref003","doi-asserted-by":"publisher","DOI":"10.1145\/1964897.1964918"},{"key":"ref004","doi-asserted-by":"publisher","DOI":"10.1109\/ISCC.2016.7543749"},{"key":"ref005","first-page":"193","author":"Sinharay A.","year":"2016","journal-title":"Proceedings of the 2016 IEEE First International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)"},{"key":"ref006","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2676168"},{"key":"ref007","doi-asserted-by":"publisher","DOI":"10.3390\/s140610146"},{"key":"ref008","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2015.01.031"},{"key":"ref009","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-27004-3"},{"key":"ref010","doi-asserted-by":"publisher","DOI":"10.3390\/s150102059"},{"key":"ref011","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-28765-7_75"},{"key":"ref012","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-35395-6_30"},{"key":"ref013","author":"Alman A.","year":"2019","journal-title":"1st International Conference on Science and Technology, ICOST 2019"},{"key":"ref014","doi-asserted-by":"publisher","DOI":"10.1145\/1964897.1964918"},{"issue":"3","key":"ref015","volume":"8","author":"NJ S.A.","year":"2017","journal-title":"International Journal of Advanced Research in Computer Science"},{"key":"ref016","doi-asserted-by":"publisher","DOI":"10.1007\/s10044-016-0549-8"},{"key":"ref017","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-014-0095-0"},{"key":"ref018","author":"Lu W.","year":"2018","journal-title":"Proc., IEEE Internet of Things Journal"},{"key":"ref019","doi-asserted-by":"publisher","DOI":"10.1109\/CBD.2013.19"},{"key":"ref020","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-017-0513-5"},{"key":"ref021","unstructured":"ReissA., HendebyG. and StrickerD., A competitive approach for human activity recognition on smartphones, in European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2013), 24-26 April, Bruges, Belgium, (2013), pp. 455\u2013460. 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