Abstract
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.
Original language | English |
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Title of host publication | 2014 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2014 |
Pages | 182-187 |
Number of pages | 6 |
Volume | 2014-January |
Edition | January |
DOIs | |
Publication status | Published - 2014 |
Externally published | Yes |
Event | IEEE International Conference on Systems, Man and Cybernetics 2014 - San Diego, United States of America Duration: 5 Oct 2014 → 8 Oct 2014 https://ieeexplore.ieee.org/xpl/conhome/6960119/proceeding (Proceedings) |
Publication series
Name | Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics |
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ISSN (Print) | 1062-922X |
Conference
Conference | IEEE International Conference on Systems, Man and Cybernetics 2014 |
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Abbreviated title | SMC 2014 |
Country/Territory | United States of America |
City | San Diego |
Period | 5/10/14 → 8/10/14 |
Internet address |
Keywords
- Classification and regression tree
- Fuzzy min-max neural network
- Human activity recognition
- Rule extraction