Rough Set Based Unsupervised Feature Selection in Mammogram Image Classification Using Entropy Measure

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, since some of the features may be redundant, and others may be irrelevant and noisy. In this paper, a novel unsupervised feature selection method using rough set based entropy measures is proposed. A typical mammogram image processing system generally consists of image acquisition, pre-processing, segmentation, feature extraction and selection, and classification. The proposed unsupervised feature selection method is compared with different supervised feature selection methods and evaluated with fuzzy c-means clustering inorder to prove the efficiency in the domain of mammogram image classification.