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FUIN
2002
123views more  FUIN 2002»
13 years 7 months ago
Learning Rough Set Classifiers from Gene Expressions and Clinical Data
Biological research is currently undergoing a revolution. With the advent of microarray technology the behavior of thousands of genes can be measured simultaneously. This capabilit...
Herman Midelfart, Henryk Jan Komorowski, Kristin N...
GECCO
2003
Springer
191views Optimization» more  GECCO 2003»
14 years 24 days ago
Artificial Immune System for Classification of Gene Expression Data
DNA microarray experiments generate thousands of gene expression measurement simultaneously. Analyzing the difference of gene expression in cell and tissue samples is useful in dia...
Shin Ando, Hitoshi Iba
GECCO
2005
Springer
156views Optimization» more  GECCO 2005»
14 years 1 months ago
Extraction of informative genes from microarray data
Identification of those genes that might anticipate the clinical behavior of different types of cancers is challenging due to availability of a smaller number of patient samples...
Topon Kumar Paul, Hitoshi Iba
CEC
2007
IEEE
13 years 11 months ago
Gene selection in cancer classification using PSO/SVM and GA/SVM hybrid algorithms
In this work we compare the use of a Particle Swarm Optimization (PSO) and a Genetic Algorithm (GA) (both augmented with Support Vector Machines SVM) for the classification of high...
Enrique Alba, José García-Nieto, Lae...
IJBRA
2007
97views more  IJBRA 2007»
13 years 7 months ago
Structural Risk Minimisation based gene expression profiling analysis
: For microarray based cancer classification, feature selection is a common method for improving classifier generalisation. Most wrapper methods use cross validation methods to eva...
Xue-wen Chen, Byron Gerlach, Dechang Chen, ZhenQiu...