Hybrid (Generalization-Correlation) Method for Feature Selection in High Dimensional DNA Microarray Prediction Problems

2011 ◽  
pp. 202-209 ◽  
Author(s):  
Yasel Couce ◽  
Leonardo Franco ◽  
Daniel Urda ◽  
José L. Subirats ◽  
José M. Jerez
Keyword(s):  
Feature Selection ◽  
Dna Microarray ◽  
Correlation Method ◽  
High Dimensional ◽  
Prediction Problems

Feature Selection Algorithms for Mining High Dimensional DNA Microarray Data

2011 ◽  
pp. 685-710 ◽  
Author(s):  
David J. Dittman ◽  
Taghi M. Khoshgoftaar ◽  
Randall Wald ◽  
Jason Van Hulse
Keyword(s):  
Feature Selection ◽  
Dna Microarray ◽  
Microarray Data ◽  
High Dimensional ◽  
Dna Microarray Data ◽  
Selection Algorithms

scholarly journals Feature Selection for high Dimensional DNA Microarray data using hybrid approaches

2013 ◽  
Vol 9 (16) ◽  
pp. 824-828 ◽  
Author(s):  
Ammu Prasanna Kumar ◽  
◽  
Preeja Valsala
Keyword(s):  
Feature Selection ◽  
Dna Microarray ◽  
Microarray Data ◽  
High Dimensional ◽  
Dna Microarray Data ◽  
Hybrid Approaches ◽  
Selection For

Scatter search for high-dimensional feature selection using feature grouping

2021 ◽  
Author(s):  
Miguel García-Torres ◽  
Francisco Gómez-Vela ◽  
Federico Divina ◽  
Diego P. Pinto-Roa ◽  
José Luis Vázquez Noguera ◽  
...  
Keyword(s):  
Feature Selection ◽  
Scatter Search ◽  
High Dimensional ◽  
Feature Grouping

CLASSIFICATION OF HIGH-DIMENSIONAL MICROARRAY DATA WITH A TWO-STEP PROCEDURE VIA A WILCOXON CRITERION AND MULTILAYER PERCEPTRON

2011 ◽  
Vol 10 (01) ◽  
pp. 1-14
Author(s):  
VLADIMIR NIKULIN ◽  
TIAN-HSIANG HUANG ◽  
GEOFFREY J. MCLACHLAN
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
High Dimensional ◽  
Second Step ◽  
Support Vector ◽  
Step Procedure ◽  
Leave One Out ◽  
Natural Combination ◽  
Feature Selection Techniques

The method presented in this paper is novel as a natural combination of two mutually dependent steps. Feature selection is a key element (first step) in our classification system, which was employed during the 2010 International RSCTC data mining (bioinformatics) Challenge. The second step may be implemented using any suitable classifier such as linear regression, support vector machine or neural networks. We conducted leave-one-out (LOO) experiments with several feature selection techniques and classifiers. Based on the LOO evaluations, we decided to use feature selection with the separation type Wilcoxon-based criterion for all final submissions. The method presented in this paper was tested successfully during the RSCTC data mining Challenge, where we achieved the top score in the Basic track.

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