Feature Selections Using Minimal Redundancy Maximal Relevance Algorithm for Human Activity Recognition in Smart Home Environments
Joint Authors
Tang, Pei
Fang, Hongqing
Si, Hao
Source
Journal of Healthcare Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-11-29
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
In this paper, maximal relevance measure and minimal redundancy maximal relevance (mRMR) algorithm (under D-R and D/R criteria) have been applied to select features and to compose different features subsets based on observed motion sensor events for human activity recognition in smart home environments.
And then, the selected features subsets have been evaluated and the activity recognition accuracy rates have been compared with two probabilistic algorithms: naïve Bayes (NB) classifier and hidden Markov model (HMM).
The experimental results show that not all features are beneficial to human activity recognition and different features subsets yield different human activity recognition accuracy rates.
Furthermore, even the same features subset has different effect on human activity recognition accuracy rate for different activity classifiers.
It is significant for researchers performing human activity recognition to consider both relevance between features and activities and redundancy among features.
Generally, both maximal relevance measure and mRMR algorithm are feasible for feature selection and positive to activity recognition.
American Psychological Association (APA)
Fang, Hongqing& Tang, Pei& Si, Hao. 2020. Feature Selections Using Minimal Redundancy Maximal Relevance Algorithm for Human Activity Recognition in Smart Home Environments. Journal of Healthcare Engineering،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1186596
Modern Language Association (MLA)
Fang, Hongqing…[et al.]. Feature Selections Using Minimal Redundancy Maximal Relevance Algorithm for Human Activity Recognition in Smart Home Environments. Journal of Healthcare Engineering No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1186596
American Medical Association (AMA)
Fang, Hongqing& Tang, Pei& Si, Hao. Feature Selections Using Minimal Redundancy Maximal Relevance Algorithm for Human Activity Recognition in Smart Home Environments. Journal of Healthcare Engineering. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1186596
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1186596