A Robust Supervised Variable Selection for Noisy High-Dimensional Data

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F15%3A00444727" target="_blank" >RIV/67985807:_____/15:00444727 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21460/15:00231271

Result on the web

<a href="http://dx.doi.org/10.1155/2015/320385" target="_blank" >http://dx.doi.org/10.1155/2015/320385</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1155/2015/320385" target="_blank" >10.1155/2015/320385</a>

Result language

angličtina

Original language name

A Robust Supervised Variable Selection for Noisy High-Dimensional Data

Original language description

The Minimum Redundancy Maximum Relevance (MRMR) approach to supervised variable selection represents a successful methodology for dimensionality reduction, which is suitable for high-dimensional data observed in two or more different groups. Various available versions of the MRMR approach have been designed to search for variables with the largest relevance for a classification task while controlling for redundancy of the selected set of variables. However, usual relevance and redundancy criteria have the disadvantages of being too sensitive to the presence of outlying measurements and/or being inefficient. We propose a novel approach called Minimum Regularized Redundancy Maximum Robust Relevance (MRRMRR), suitable for noisy high-dimensional data observed in two groups. It combines principles of regularization and robust statistics. Particularly, redundancy is measured by a new regularized version of the coefficient of multiple correlation and relevance is measured by a highly robust c

Czech name

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Czech description

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Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

BA - General mathematics

OECD FORD branch

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Project

<a href="/en/project/GA13-17187S" target="_blank" >GA13-17187S: Constructing Advanced Comprehensible Classifiers</a><br>

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2015

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

BioMed Research International

ISSN

2314-6133

e-ISSN

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Volume of the periodical

2015

Issue of the periodical within the volume

Article 320385

Country of publishing house

US - UNITED STATES

Number of pages

10

Pages from-to

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UT code for WoS article

000356261700001

EID of the result in the Scopus database

2-s2.0-84934967829