Feature selection and combination for stress identification using correlation and diversity

Yong Deng, D. Frank Hsu, Zhonghai Wu, Chao Hsien Chu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Scopus citations

Abstract

Using multiple physiological sensors to detect different stress level has become an important and popular task in improving human health and well-being. In the process, the selection of a smaller set of independent features is a necessary, yet challenging, step for feature combination, situation analysis and decision making. In this paper, we investigate feature selection methods using both concepts of correlation and diversity. Six feature combination methods (C4.5, Naïve Bayes, Linear Discriminant Function, Support Vector Machine, K Nearest Neighbors and Combinatorial Fusion) are applied to the selected features in the detection of the stress levels. Our results demonstrated that (a) diversity based feature selection is as good as correlation based selection across all six combination methods, and (b) combinatorial fusion method performs better than five other combination methods across all features selected by using both correlation and diversity.

Original languageEnglish (US)
Title of host publicationProceedings of the 2012 International Symposium on Pervasive Systems, Algorithms, and Networks, I-SPAN 2012
Pages37-43
Number of pages7
DOIs
StatePublished - Dec 1 2012
Event12th International Symposium on Pervasive Systems, Algorithms, and Networks, I-SPAN 2012 - San Marcos, TX, United States
Duration: Dec 13 2012Dec 15 2012

Publication series

NameProceedings of the 2012 International Symposium on Pervasive Systems, Algorithms, and Networks, I-SPAN 2012

Other

Other12th International Symposium on Pervasive Systems, Algorithms, and Networks, I-SPAN 2012
Country/TerritoryUnited States
CitySan Marcos, TX
Period12/13/1212/15/12

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Applied Mathematics

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