Comparative analysis on extended belief rule-based system for activity recognition

Long-Hao Yang, Jun Liu, Ying-Ming Wang, Luis Martinez

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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 languageEnglish
Title of host publicationProceedings of the 13th International FLINS Conference (FLINS 2018)
EditorsJun Liu, Jie Lu, Yang Xu, Luis Martinez, Etienne E Kerre
PublisherWorld Scientific Publishing
ISBN (Electronic)9789813273245
ISBN (Print)9789813273221
DOIs
Publication statusPublished - 21 Aug 2018
Event13th International Conference on Data Science and Knowledge Engineering for Sensing Decision Support (FLINS 2018) - Belfast, Northern Ireland, UK, Belfast, United Kingdom
Duration: 21 Aug 201824 Aug 2018

Conference

Conference13th International Conference on Data Science and Knowledge Engineering for Sensing Decision Support (FLINS 2018)
Abbreviated titleFLINS2018
Country/TerritoryUnited Kingdom
CityBelfast
Period21/08/1824/08/18

Keywords

  • Extended belief rule-based system
  • activity recognition
  • comparative analysis

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