Multi-label classification based ensemble learning for human activity recognition in smart home
In recent times with advancements in wireless sensor technologies, human activity recognition in smart home environments have gained significant interest amongst the researchers. Numerous literatures have been focusing only on single occupant-based activity recognition because of escalating difficul...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2020
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| Online Access: | http://psasir.upm.edu.my/id/eprint/86851/ http://psasir.upm.edu.my/id/eprint/86851/1/Multi%20label%20classification%20based%20ensemble.pdf |
| _version_ | 1848860303752691712 |
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| author | Jethanandani, Manan Sharma, Abhishek Perumal, Thinagaran Chang, Jieh Ren |
| author_facet | Jethanandani, Manan Sharma, Abhishek Perumal, Thinagaran Chang, Jieh Ren |
| author_sort | Jethanandani, Manan |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | In recent times with advancements in wireless sensor technologies, human activity recognition in smart home environments have gained significant interest amongst the researchers. Numerous literatures have been focusing only on single occupant-based activity recognition because of escalating difficulties with multiple occupants within the same conditions. But having multiple occupants is more general. Therefore, in this study, the application of the Classifier Chain method of Multi-Label Classification is described to address the complicated problem of multi-resident activity recognition. Four different classifiers namely Bernoulli Naïve Bayes, Decision Tree, Logistic Regression and K-Nearest Neighbor as base classifiers for this multi-label classification approach are implemented and a comparative study of these model's performance is presented. Furthermore, the Majority Voting Ensemble Classifier method based on the ensemble learning is developed for the activity recognition problem. All these models are evaluated using several evaluation metrics on publicly available ARAS datasets. Through the results obtained from experiments, it can be deduced that the Classifier Chain method not only handles the challenges of this complex problem competently but also exhibits the importance of multi-label classification approach towards the field of activity recognition. |
| first_indexed | 2025-11-15T12:43:06Z |
| format | Article |
| id | upm-86851 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T12:43:06Z |
| publishDate | 2020 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-868512021-11-22T02:18:58Z http://psasir.upm.edu.my/id/eprint/86851/ Multi-label classification based ensemble learning for human activity recognition in smart home Jethanandani, Manan Sharma, Abhishek Perumal, Thinagaran Chang, Jieh Ren In recent times with advancements in wireless sensor technologies, human activity recognition in smart home environments have gained significant interest amongst the researchers. Numerous literatures have been focusing only on single occupant-based activity recognition because of escalating difficulties with multiple occupants within the same conditions. But having multiple occupants is more general. Therefore, in this study, the application of the Classifier Chain method of Multi-Label Classification is described to address the complicated problem of multi-resident activity recognition. Four different classifiers namely Bernoulli Naïve Bayes, Decision Tree, Logistic Regression and K-Nearest Neighbor as base classifiers for this multi-label classification approach are implemented and a comparative study of these model's performance is presented. Furthermore, the Majority Voting Ensemble Classifier method based on the ensemble learning is developed for the activity recognition problem. All these models are evaluated using several evaluation metrics on publicly available ARAS datasets. Through the results obtained from experiments, it can be deduced that the Classifier Chain method not only handles the challenges of this complex problem competently but also exhibits the importance of multi-label classification approach towards the field of activity recognition. Elsevier 2020-12 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/86851/1/Multi%20label%20classification%20based%20ensemble.pdf Jethanandani, Manan and Sharma, Abhishek and Perumal, Thinagaran and Chang, Jieh Ren (2020) Multi-label classification based ensemble learning for human activity recognition in smart home. Internet of Things, 12. art. no. 100324. pp. 1-13. ISSN 2542-6605 https://www.sciencedirect.com/science/article/abs/pii/S2542660520301554 10.1016/j.iot.2020.100324 |
| spellingShingle | Jethanandani, Manan Sharma, Abhishek Perumal, Thinagaran Chang, Jieh Ren Multi-label classification based ensemble learning for human activity recognition in smart home |
| title | Multi-label classification based ensemble learning for human activity recognition in smart home |
| title_full | Multi-label classification based ensemble learning for human activity recognition in smart home |
| title_fullStr | Multi-label classification based ensemble learning for human activity recognition in smart home |
| title_full_unstemmed | Multi-label classification based ensemble learning for human activity recognition in smart home |
| title_short | Multi-label classification based ensemble learning for human activity recognition in smart home |
| title_sort | multi-label classification based ensemble learning for human activity recognition in smart home |
| url | http://psasir.upm.edu.my/id/eprint/86851/ http://psasir.upm.edu.my/id/eprint/86851/ http://psasir.upm.edu.my/id/eprint/86851/ http://psasir.upm.edu.my/id/eprint/86851/1/Multi%20label%20classification%20based%20ensemble.pdf |