Unsupervised feature selection based on the measures of degree of dependency using rough set theory in digital mammogram image classification

Abstract

Feature Selection (FS) has become one of the most active research topics in the area of data mining. It performs to remove redundant and noisy features from high-dimensional data sets. A good feature selection has several advantages for a learning algorithm such as reducing computational cost, increasing its classification accuracy and improving result comprehensibility. 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 rough set based measures, is proposed. A typical mammogram image processing system generally consists of mammogram image acquisition, preprocessing 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 existing rough set based supervised feature selection methods and classification performance of both methods are recorded and demonstrates the efficiency of the method.