A hybrid FMM-CART model for human activity recognition
Data(s) |
01/01/2014
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Resumo |
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. |
Identificador | |
Idioma(s) |
eng |
Publicador |
IEEE |
Relação |
http://dro.deakin.edu.au/eserv/DU:30076124/lim-evidenceieeeconfsmc-2014.pdf http://dro.deakin.edu.au/eserv/DU:30076124/lim-hybridfmmcartmodel-2014.pdf http://www.dx.doi.org/10.1109/SMC.2014.6973904 |
Direitos |
2014, IEEE |
Palavras-Chave | #classification and regression tree #fuzzy min-max neural network #human activity recognition #rule extraction |
Tipo |
Conference Paper |