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GECCO
2007
Springer
179views Optimization» more  GECCO 2007»
14 years 27 days ago
Evolutionary selection of minimum number of features for classification of gene expression data using genetic algorithms
Selecting the most relevant factors from genetic profiles that can optimally characterize cellular states is of crucial importance in identifying complex disease genes and biomark...
Alper Küçükural, Reyyan Yeniterzi...
CIBCB
2005
IEEE
14 years 10 days ago
Two-Phase EA/k-NN for Feature Selection and Classification in Cancer Microarray Datasets
Efficient and reliable methods that can find a small sample of informative genes amongst thousands are of great importance. In this area, much research is investigating the combina...
Thorhildur Juliusdottir, David Corne, Ed Keedwell,...
ICTAI
1992
IEEE
13 years 10 months ago
Genetic Algorithms as a Tool for Feature Selection in Machine Learning
This paper describes an approach being explored to improve the usefulness of machine learning techniques for generating classification rules for complex, real world data. The appr...
Haleh Vafaie, Kenneth A. De Jong
KBS
2008
98views more  KBS 2008»
13 years 5 months ago
Mixed feature selection based on granulation and approximation
Feature subset selection presents a common challenge for the applications where data with tens or hundreds of features are available. Existing feature selection algorithms are mai...
Qinghua Hu, Jinfu Liu, Daren Yu
AIME
2009
Springer
13 years 10 months ago
The Role of Biomedical Dataset in Classification
In this paper, we investigate the role of a biomedical dataset on the classification accuracy of an algorithm. We quantify the complexity of a biomedical dataset using five complex...
Ajay Kumar Tanwani, Muddassar Farooq