Fuzzy Discretization for Rough Set Based Gene Selection Algorithm

2011 ◽  
Author(s):  
Sushmita Paul ◽  
Pradipta Maji
Keyword(s):  
Rough Set ◽  
Gene Selection ◽  
Selection Algorithm ◽  
Gene Selection Algorithm

A Comparative Analysis of Rough Set Based Intelligent Techniques for Unsupervised Gene Selection

2013 ◽  
Vol 2 (4) ◽  
pp. 33-46 ◽  
Author(s):  
P. K. Nizar Banu ◽  
H. Hannah Inbarani
Keyword(s):  
Data Mining ◽  
Rough Set ◽  
Gene Selection ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Huge Number ◽  
Number Of Genes ◽  
Micro Array ◽  
Tolerance Rough Set ◽  
Array Databases

As the micro array databases increases in dimension and results in complexity, identifying the most informative genes is a challenging task. Such difficulty is often related to the huge number of genes with very few samples. Research in medical data mining addresses this problem by applying techniques from data mining and machine learning to the micro array datasets. In this paper Unsupervised Tolerance Rough Set based Quick Reduct (U-TRS-QR), a diverse feature selection algorithm, which extends the existing equivalent rough sets for unsupervised learning, is proposed. Genes selected by the proposed method leads to a considerably improved class predictions in wide experiments on two gene expression datasets: Brain Tumor and Colon Cancer. The results indicate consistent improvement among 12 classifiers.

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.

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