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Support vector machine / Naive Bayes classifier / Classifier / Binary classification / Supervised learning / Feature selection / Linear discriminant analysis / Cross-validation / Random subspace method / Statistics / Machine learning / Statistical classification


SlimPLS: A Method for Feature Selection in Gene Expression-Based Disease Classification Michael Gutkin1, Ron Shamir1*, Gideon Dror2 1 Blavatnik school of Computer Science, Tel Aviv University, Tel Aviv, Israel, 2 School
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Document Date: 2010-08-18 18:53:13

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File Size: 907,16 KB

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City

Gene Expression / Tel-Aviv / SlimPLS Ranking / /

Company

Pearson / Creative Commons / /

Country

Israel / /

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Facility

University of Manchester / Tel Aviv University / The Academic College of Tel-Aviv-Yaffo / /

IndustryTerm

pre-processing step / matrix products / hill climbing search / local search / feature selection algorithm / classification algorithms / pre-processing stage / prediction algorithm / feature selection algorithms / basic algorithm / learning algorithm / learning algorithms / /

NaturalFeature

Random Forest / /

Organization

Academic College of Tel-Aviv-Yaffo / School of Computer Science / European Commission / University of Manchester / Tel Aviv University / Tel Aviv / /

Person

Herman Wold / /

Position

original author / Editor / using Fisher / Cao / candidate for overcoming these problems / /

ProgrammingLanguage

E / /

ProvinceOrState

New Brunswick / /

PublishedMedium

PLoS ONE / the PLoS ONE / /

Technology

e6416 SlimPLS Feature Selection classification algorithms / PLS algorithm / Partial Least Squares algorithm / 36 feature selection algorithms / DNA Chip / Gene Expression / prediction algorithm / classification algorithms / feature selection algorithm / detailed learning algorithm / basic algorithm / /

URL

www.plosone.org / /

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