NDLI: Combining feature ranking with PCA: An application to gait analysis

Content Provider	IEEE Xplore Digital Library
Author	Ming-Jing Yang Hui-Ru Zheng Hai-Ying Wang Mcclean, S. Harris, N.
Copyright Year	2010
Description	Author affiliation: Computer Science Research Institute, School of Computing and Mathematics, University of Ulster, UK (Ming-Jing Yang; Hui-Ru Zheng; Hai-Ying Wang) \|\| Computer Science Research Institute, School of Computing and Information Engineering, University of Ulster, UK (Mcclean, S.) \|\| School for Health, University of Bath, UK (Harris, N.)
Abstract	Feature reduction is an effective way to improve the classification performance when machine learning methods are used in gait analysis. In this paper, we proposed a novel hybrid feature reduction method (MSNR&PCA) based on the combination of feature ranking with principle component analysis (PCA). Three feature reduction methods, namely, feature ranking based the value of signal to noise ratio (MSNR), PCA and the proposed hybrid approach (MSNR&PCA), were examined in two gait analysis problems. One gait analysis problem is to differentiate the patients with Neurodegenerative disease from the controls based on the gait data collected by footswitches. The other problem is to discriminate the patients with complex regional pain syndrome (CRPS) from controls based on the gait data collected by an accelerometer. Results showed that the proposed MSNR&PCA achieved best classification performance in two gait datasets. In footswitch data, the highest accuracy (81.78%) was obtained using a feature subset with 4 features generated from original 10 features by MSNR&PCA. In the accelerometer dataset, classification with three features generated from 17 features by MSNR&PCA achieved the best performance with an accuracy of 100%.
Starting Page	494
Ending Page	499
File Size	134314
Page Count	6
File Format	PDF
ISBN	9781424465262
e-ISBN	9781424465279
DOI	10.1109/ICMLC.2010.5581013
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-11
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Principal component analysis Accuracy Signal to noise ratio Accelerometers Feature extraction Diseases Acceleration Foot switch Gait Analysis Feature selection Accelerometer
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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