Abstract
One of eminent activity recognition approaches is based on the extended belief rule-based system (EBRBS), which shows an excellent robustness comparing with many traditional approaches. For this reason, three versions of EBRBSs, namely original EBRBS (O-EBRBS), DRA-EBRBS, and Micro-EBRBS, are adapted in the comparative analysis to illustrate the performance of these EBRBSs. Results demonstrate that the Micro-EBRBS can produce a satisfied accuracy under less time of inference scheme and fewer numbers of activated rules comparing to the O-EBRBS and DRA-EBRBS.
Original language | English |
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Title of host publication | Proceedings of the 13th International FLINS Conference (FLINS 2018) |
Editors | Jun Liu, Jie Lu, Yang Xu, Luis Martinez, Etienne E Kerre |
Publisher | World Scientific Publishing |
ISBN (Electronic) | 9789813273245 |
ISBN (Print) | 9789813273221 |
DOIs | |
Publication status | Published (in print/issue) - 21 Aug 2018 |
Event | 13th International Conference on Data Science and Knowledge Engineering for Sensing Decision Support (FLINS 2018) - Belfast, Northern Ireland, UK, Belfast, United Kingdom Duration: 21 Aug 2018 → 24 Aug 2018 |
Conference
Conference | 13th International Conference on Data Science and Knowledge Engineering for Sensing Decision Support (FLINS 2018) |
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Abbreviated title | FLINS2018 |
Country/Territory | United Kingdom |
City | Belfast |
Period | 21/08/18 → 24/08/18 |
Keywords
- Extended belief rule-based system
- activity recognition
- comparative analysis