A hybrid FMM-CART model for human activity recognition

Manjeevan Seera, Chu Kiong Loo, Chee Peng Lim

Research output: Chapter in Book/Report/Conference proceedingConference PaperOtherpeer-review

7 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 languageEnglish
Title of host publication2014 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2014
Pages182-187
Number of pages6
Volume2014-January
EditionJanuary
DOIs
Publication statusPublished - 2014
Externally publishedYes
EventIEEE International Conference on Systems, Man and Cybernetics 2014 - San Diego, United States of America
Duration: 5 Oct 20148 Oct 2014
https://ieeexplore.ieee.org/xpl/conhome/6960119/proceeding (Proceedings)

Publication series

NameConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
ISSN (Print)1062-922X

Conference

ConferenceIEEE International Conference on Systems, Man and Cybernetics 2014
Abbreviated titleSMC 2014
CountryUnited States of America
CitySan Diego
Period5/10/148/10/14
Internet address

Keywords

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

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