Title: Entropy based unsupervised Feature Selection in digital mammogram image using rough set theory
Authors: C. Velayutham; K. Thangavel
Addresses: Department of Computer Science, Aditanar College, Tiruchendur, Thoothukudi, Tamil Nadu 628216, India. ' Department of Computer Science, Periyar University, Salem, Tamil Nadu 636011, India
Abstract: Feature Selection (FS) is a process, which attempts to select features, which are more informative. 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 FS. 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 FS in mammogram image, using rough set-based entropy measures, is proposed. A typical mammogram image processing system generally consists of mammogram image acquisition, pre-processing of image, segmentation, features extracted from the segmented mammogram image. The proposed method is used to select features from data set, the method is compared with the existing rough set-based supervised FS methods and classification performance of both methods are recorded and demonstrates the efficiency of the method.
Keywords: unsupervised feature selection; mammogram images; texture description matrix; data mining; RST; rough set theory; entropy; classification performance; breast cancer; mammography.
International Journal of Computational Biology and Drug Design, 2012 Vol.5 No.1, pp.16 - 34
Received: 08 May 2021
Accepted: 12 May 2021
Published online: 16 Mar 2012 *