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http://dx.doi.org/10.9723/jksiis.2016.21.6.013

A Diagnostic Feature Subset Selection of Breast Tumor Based on Neighborhood Rough Set Model  

Son, Chang-Sik (DGIST 웰니스융합연구센터)
Choi, Rock-Hyun (DGIST 웰니스융합연구센터)
Kang, Won-Seok (DGIST 웰니스융합연구센터)
Lee, Jong-Ha (계명대학교 의과대학 의용공학과)
Publication Information
Journal of the Korea Industrial Information Systems Research / v.21, no.6, 2016 , pp. 13-21 More about this Journal
Abstract
Feature selection is the one of important issue in the field of data mining and machine learning. It is the technique to find a subset of features which provides the best classification performance, from the source data. We propose a feature subset selection method using the neighborhood rough set model based on information granularity. To demonstrate the effectiveness of proposed method, it was applied to select the useful features associated with breast tumor diagnosis of 298 shape features extracted from 5,252 breast ultrasound images, which include 2,745 benign and 2,507 malignant cases. Experimental results showed that 19 diagnostic features were strong predictors of breast cancer diagnosis and then average classification accuracy was 97.6%.
Keywords
Neighborhood Rough Set; Neighborhood Approximations; Feature Selection; Breast Tumor Diagnosis;
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Times Cited By KSCI : 3  (Citation Analysis)
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