A hybrid FMM-CART model for human activity recognition
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Version 1 2015-08-21, 11:28Version 1 2015-08-21, 11:28
conference contribution
posted on 2024-06-06, 08:06authored byM Seera, CK Loo, Chee Peng Lim
In this paper, the application of a hybrid model combining the fuzzy min-max (FMM) neural network and the classification and regression tree (CART) to human activity recognition is presented. The hybrid FMM-CART model capitalizes the merits of both FMM and CART in data classification and rule extraction. To evaluate the effectiveness of FMM-CART, two data sets related to human activity recognition problems are conducted. The results obtained are higher than those reported in the literature. More importantly, practical rules in the form of a decision tree are extracted to provide explanation and justification for the predictions from FMM- CART. This outcome positively indicates the potential of FMM- CART in undertaking human activity recognition tasks.
History
Pagination
182-187
Location
San Diego, Calif.
Start date
2014-10-05
End date
2014-10-08
ISSN
1062-922X
Language
eng
Publication classification
E Conference publication, E1 Full written paper - refereed
Copyright notice
2014, IEEE
Title of proceedings
IEEE 2014 : Proceedings of the International Conference on Systems, Man and Cybernetics (SMC)
Event
Systems, Man and Cybernetics (SMC). IEEE International Conference (2014 : San Diego, Calif.)