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BMCBI
2006
198views more  BMCBI 2006»
15 years 5 months ago
Gene selection and classification of microarray data using random forest
Background: Selection of relevant genes for sample classification is a common task in most gene expression studies, where researchers try to identify the smallest possible set of ...
Ramón Díaz-Uriarte, Sara Alvarez de ...
ICML
2009
IEEE
15 years 12 months ago
Non-monotonic feature selection
We consider the problem of selecting a subset of m most informative features where m is the number of required features. This feature selection problem is essentially a combinator...
Zenglin Xu, Rong Jin, Jieping Ye, Michael R. Lyu, ...
157
Voted
ESANN
2008
15 years 6 months ago
A method for robust variable selection with significance assessment
Our goal is proposing an unbiased framework for gene expression analysis based on variable selection combined with a significance assessment step. We start by discussing the need ...
Annalisa Barla, Sofia Mosci, Lorenzo Rosasco, Ales...
182
Voted
KDD
2010
ACM
274views Data Mining» more  KDD 2010»
15 years 9 months ago
Grafting-light: fast, incremental feature selection and structure learning of Markov random fields
Feature selection is an important task in order to achieve better generalizability in high dimensional learning, and structure learning of Markov random fields (MRFs) can automat...
Jun Zhu, Ni Lao, Eric P. Xing
KES
2006
Springer
15 years 5 months ago
Combined Gene Selection Methods for Microarray Data Analysis
In recent years, the rapid development of DNA Microarray technology has made it possible for scientists to monitor the expression level of thousands of genes in a single experiment...
Hong Hu, Jiuyong Li, Hua Wang, Grant Daggard