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A hybrid FMM-CART model for human activity recognition
conference contribution
posted on 2014-12-01, 00:00 authored by M Seera, C K Loo, Chee Peng LimChee Peng LimIn 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.
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Systems, Man and Cybernetics (SMC). IEEE International Conference (2014 : San Diego, Calif.)Series
Systems, Man and Cybernetics (SMC)Pagination
182 - 187Publisher
IEEELocation
San Diego, Calif.Place of publication
Piscataway, N.J.Publisher DOI
Start date
2014-10-05End date
2014-10-08ISSN
1062-922XLanguage
engPublication classification
E Conference publication; E1 Full written paper - refereedCopyright notice
2014, IEEETitle of proceedings
IEEE 2014 : Proceedings of the International Conference on Systems, Man and Cybernetics (SMC)Usage metrics
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