A hybrid FMM-CART model for human activity recognition

Manjeevan Seera; Chu Kiong Loo; Chee Peng Lim

doi:10.1109/SMC.2014.6973904

A hybrid FMM-CART model for human activity recognition

Manjeevan Seera, Chu Kiong Loo, Chee Peng Lim

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Other › peer-review

13 Citations (Scopus)

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
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	https://doi.org/10.1109/SMC.2014.6973904
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
ISSN (Print)	1062-922X

Conference

Conference	IEEE International Conference on Systems, Man and Cybernetics 2014
Abbreviated title	SMC 2014
Country/Territory	United States of America
City	San Diego
Period	5/10/14 → 8/10/14
Internet address	https://ieeexplore.ieee.org/xpl/conhome/6960119/proceeding (Proceedings)

Keywords

Classification and regression tree
Fuzzy min-max neural network
Human activity recognition
Rule extraction

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10.1109/SMC.2014.6973904

Cite this

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title = "A hybrid FMM-CART model for human activity recognition",

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.",

keywords = "Classification and regression tree, Fuzzy min-max neural network, Human activity recognition, Rule extraction",

author = "Manjeevan Seera and Loo, {Chu Kiong} and Lim, {Chee Peng}",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE. Copyright: Copyright 2015 Elsevier B.V., All rights reserved.; IEEE International Conference on Systems, Man and Cybernetics 2014, SMC 2014 ; Conference date: 05-10-2014 Through 08-10-2014",

year = "2014",

doi = "10.1109/SMC.2014.6973904",

language = "English",

volume = "2014-January",

series = "Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics",

pages = "182--187",

booktitle = "2014 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2014",

edition = "January",

url = "https://ieeexplore.ieee.org/xpl/conhome/6960119/proceeding",

}

Seera, M, Loo, CK & Lim, CP 2014, A hybrid FMM-CART model for human activity recognition. in 2014 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2014. January edn, vol. 2014-January, Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, pp. 182-187, IEEE International Conference on Systems, Man and Cybernetics 2014, San Diego, California, United States of America, 5/10/14. https://doi.org/10.1109/SMC.2014.6973904

A hybrid FMM-CART model for human activity recognition. / Seera, Manjeevan; Loo, Chu Kiong; Lim, Chee Peng.
2014 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2014. Vol. 2014-January January. ed. 2014. p. 182-187 (Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Other › peer-review

TY - GEN

T1 - A hybrid FMM-CART model for human activity recognition

AU - Seera, Manjeevan

AU - Loo, Chu Kiong

AU - Lim, Chee Peng

PY - 2014

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N2 - 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.

AB - 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.

KW - Classification and regression tree

KW - Fuzzy min-max neural network

KW - Human activity recognition

KW - Rule extraction

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M3 - Conference Paper

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BT - 2014 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2014

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A hybrid FMM-CART model for human activity recognition

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Keywords

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