Unsupervised Feature Selection in Digital Mammogram Image Using Tolerance Rough Set Based Quick Reduct

2012 ◽  
Author(s):  
I. Laurence Aroquiaraj ◽  
K. Thangavel
Keyword(s):  
Feature Selection ◽  
Rough Set ◽  
Digital Mammogram ◽  
Unsupervised Feature Selection ◽  
Tolerance Rough Set ◽  
Mammogram Image ◽  
Quick Reduct

Hybrid Tolerance Rough Set

2013 ◽  
Vol 3 (4) ◽  
pp. 15-30 ◽  
Author(s):  
G. Jothi ◽  
H. Hannah Inbarani ◽  
Ahmad Taher Azar
Keyword(s):  
Breast Cancer ◽  
Feature Selection ◽  
Diagnostic Accuracy ◽  
Rough Set ◽  
Information Loss ◽  
Discretization Method ◽  
Data Set ◽  
Tolerance Rough Set ◽  
Mammogram Images ◽  
Quick Reduct

Breast cancer is the most common malignant tumor found among young and middle aged women. Feature Selection is a process of selecting most enlightening features from the data set which preserves the original significance of the features following reduction. The traditional rough set method cannot be directly applied to deafening data. This is usually addressed by employing a discretization method, which can result in information loss. This paper proposes an approach based on the tolerance rough set model, which has the flair to deal with real-valued data whilst simultaneously retaining dataset semantics. In this paper, a novel supervised feature selection in mammogram images, using Tolerance Rough Set - PSO based Quick Reduct (STRSPSO-QR) and Tolerance Rough Set - PSO based Relative Reduct (STRSPSO-RR), is proposed. The results obtained using the proposed methods show an increase in the diagnostic accuracy.

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