NDLI: Evaluating feature selection strategies for high dimensional, small sample size datasets

Content Provider	IEEE Xplore Digital Library
Author	Golugula, Abhishek Lee, George Madabhushi, Anant
Copyright Year	2011
Description	Author affiliation: Department of Electrical and Computer Engineering, Rutgers University, Piscataway, New Jersey 08854 (Golugula, Abhishek) \|\| Department of Biomedical Engineering, Rutgers University, Piscataway, New Jersey 08854 (Lee, George; Madabhushi, Anant)
Abstract	In this work, we analyze and evaluate different strategies for comparing Feature Selection (FS) schemes on High Dimensional (HD) biomedical datasets (e.g. gene and protein expression studies) with a small sample size (SSS). Additionally, we define a new feature, Robustness, specifically for comparing the ability of an FS scheme to be invariant to changes in its training data. While classifier accuracy has been the de facto method for evaluating FS schemes, on account of the curse of dimensionality problem, it might not always be the appropriate measure for HD/SSS datasets. SSS lends the dataset a higher probability of containing data that is not representative of the true distribution of the whole population. However, an ideal FS scheme must be robust enough to produce the same results each time there are changes to the training data. In this study, we employed the robustness performance measure in conjunction with classifier accuracy (measured via the K-Nearest Neighbor and Random Forest classifiers) to quantitatively compare five different FS schemes (T-test, F-test, Kolmogorov-Smirnov Test, Wilks Lambda Test and Wilcoxon Rand Sum Test) on 5 HD/SSS gene and protein expression datasets corresponding to ovarian cancer, lung cancer, bone lesions, celiac disease, and coronary heart disease. Of the five FS schemes compared, the Wilcoxon Rand Sum Test was found to outperform other FS schemes in terms of classification accuracy and robustness. Our results suggest that both classifier accuracy and robustness should be considered when deciding on the appropriate FS scheme for HD/SSS datasets.
Starting Page	949
Ending Page	952
File Size	553896
Page Count	4
File Format	PDF
ISBN	9781424441211
ISSN	1557170X
e-ISBN	9781457715891
e-ISBN	9781424441228
DOI	10.1109/IEMBS.2011.6090214
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	High definition video Robustness Feature extraction Accuracy Cancer Diseases Proteins
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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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