New gene selection algorithm using hypeboxes to improve performance of classifiers

2020 ◽  
Vol 16 (3) ◽  
pp. 269
Author(s):  
Adil M. Bagirov ◽  
Karim Mardaneh
Keyword(s):  
Gene Selection ◽  
Selection Algorithm ◽  
Improve Performance ◽  
New Gene ◽  
Gene Selection Algorithm

REGULARIZATION NETWORK-BASED GENE SELECTION FOR MICROARRAY DATA ANALYSIS

2006 ◽  
Vol 16 (05) ◽  
pp. 341-352 ◽  
Author(s):  
XIN ZHOU ◽  
K. Z. MAO
Keyword(s):  
Feature Selection ◽  
Microarray Data ◽  
Gene Selection ◽  
Microarray Data Analysis ◽  
Filter Method ◽  
Selection Algorithm ◽  
Feature Selection Problem ◽  
Classification Feature ◽  
Wrapper Method ◽  
Gene Selection Algorithm

Microarray data contains a large number of genes (usually more than 1000) and a relatively small number of samples (usually fewer than 100). This presents problems to discriminant analysis of microarray data. One way to alleviate the problem is to reduce dimensionality of data by selecting important genes to the discriminant problem. Gene selection can be cast as a feature selection problem in the context of pattern classification. Feature selection approaches are broadly grouped into filter methods and wrapper methods. The wrapper method outperforms the filter method but at the cost of more intensive computation. In the present study, we proposed a wrapper-like gene selection algorithm based on the Regularization Network. Compared with classical wrapper method, the computational costs in our gene selection algorithm is significantly reduced, because the evaluation criterion we proposed does not demand repeated training in the leave-one-out procedure.

Sign in / Sign up

Export Citation Format

Share Document