NDLI: Bayesian MMSE estimation of classification error and performance on real genomic data

Content Provider	IEEE Xplore Digital Library
Author	Dalton, L.A. Dougherty, E.R.
Copyright Year	2010
Description	Author affiliation: Dept. of Electrical and Computer Engineering, Texas A&M University, College Station, 77843 USA (Dalton, L.A.; Dougherty, E.R.)
Abstract	Small sample classifier design has become a major issue in the biological and medical communities, owing to the recent development of high-throughput genomic and proteomic technologies. And as the problem of estimating classifier error is already handicapped by limited available information, it is further compounded by the necessity of reusing training-data for error estimation. Due to the difficulty of error estimation, all currently popular techniques have been heuristically devised, rather than rigorously designed based on statistical inference and optimization. However, a recently proposed error estimator has placed the problem into an optimal mean-square error (MSE) signal estimation framework in the presence of uncertainty. This results in a Bayesian approach to error estimation based on a parameterized family of feature-label distributions. These Bayesian error estimators are optimal when averaged over a given family of distributions, unbiased when averaged over a given family and all samples, and analytically address a trade-off between robustness (modeling assumptions) and accuracy (minimum mean-square error). Closed form solutions have been provided for two important examples: the discrete classification problem and linear classification of Gaussian distributions. Here we discuss the Bayesian minimum mean-square error (MMSE) error estimator and demonstrate performance on real biological data under Gaussian modeling assumptions.
Starting Page	1
Ending Page	4
File Size	629884
Page Count	4
File Format	PDF
ISBN	9781612847917
ISSN	2150301X
e-ISBN	9781612847924
DOI	10.1109/GENSIPS.2010.5719674
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-11-10
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Error analysis Bayesian methods Estimation Genomics Data models Bioinformatics
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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