DocumentCode :
2430632
Title :
Unsupervised Feature Selection in Digital Mammogram Image Using Tolerance Rough Set Based Quick Reduct
Author :
Aroquiaraj, Laurence ; Thangavel, K.
Author_Institution :
Dept. of Comput. Sci., Periyar Univ., Salem, India
fYear :
2012
fDate :
3-5 Nov. 2012
Firstpage :
436
Lastpage :
440
Abstract :
Feature Selection (FS) aims to determine a minimal feature subset from a problem domain while retaining a suitably high accuracy in representing the original features. Rough set theory (RST) has been used as such a tool with much success. In the supervised FS methods, various feature subsets are evaluated using an evaluation function or metric to select only those features which are related to the decision classes of the data under consideration. However, for many data mining applications, decision class labels are often unknown or incomplete, thus indicating the significance of unsupervised feature selection. However, in unsupervised learning, decision class labels are not provided. The problem is that not all features are important. Some of the features may be redundant, and others may be irrelevant and noisy. In this paper, a novel unsupervised feature selection in mammogram image, using tolerance rough set based quick reduct, is proposed. A typical mammogram image processing system generally consists of mammogram image acquisition, pre-processing of image segmentation, feature extraction, feature selection and classification. The proposed method is used to select features from the extracted features and the method is compared with existing rough set based supervised feature selection methods. The proposed method is evaluated through classification algorithms in WEKA.
Keywords :
data mining; feature extraction; image classification; image segmentation; mammography; medical image processing; rough set theory; unsupervised learning; WEKA; data mining; decision class labels; digital mammogram image; feature extraction; image classification; image segmentation; mammogram image acquisition; rough set theory; tolerance rough set based quick reduct; unsupervised feature selection; unsupervised learning; Accuracy; Breast; Computer science; Educational institutions; Feature extraction; Image segmentation; Set theory; Mammography; Quick Reduct; Rough Set Theory; Tolerance Rough Set; Unsupervised Feature Selection;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence and Communication Networks (CICN), 2012 Fourth International Conference on
Conference_Location :
Mathura
Print_ISBN :
978-1-4673-2981-1
Type :
conf
DOI :
10.1109/CICN.2012.202
Filename :
6375150
Link To Document :
بازگشت