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

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.