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CSDA
2006
96views more  CSDA 2006»
13 years 4 months ago
Analysis of new variable selection methods for discriminant analysis
Several methods to select variables that are subsequently used in discriminant analysis are proposed and analysed. The aim is to find from among a set of m variables a smaller sub...
Joaquín A. Pacheco, Silvia Casado, Laura N&...
GECCO
2003
Springer
127views Optimization» more  GECCO 2003»
13 years 10 months ago
Complex Function Sets Improve Symbolic Discriminant Analysis of Microarray Data
Abstract. Our ability to simultaneously measure the expression levels of thousands of genes in biological samples is providing important new opportunities for improving the diagnos...
David M. Reif, Bill C. White, Nancy Olsen, Thomas ...
GFKL
2005
Springer
105views Data Mining» more  GFKL 2005»
13 years 10 months ago
Variable Selection for Discrimination of More Than Two Classes Where Data are Sparse
In classification, with an increasing number of variables, the required number of observations grows drastically. In this paper we present an approach to put into effect the maxi...
Gero Szepannek, Claus Weihs
EOR
2011
172views more  EOR 2011»
12 years 11 months ago
Efficiency measurement using independent component analysis and data envelopment analysis
Efficiency measurement is an important issue for any firm or organization. Efficiency measurement allows organizations to compare their performance with their competitors’ and t...
Ling-Jing Kao, Chi-Jie Lu, Chih-Chou Chiu
ESANN
2008
13 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...