Enhanced Rough Sets Rule Reduction Algorithm for Classification Digital Mammography Aboul Ella Hassanien and Jafar Μ. H. Ali. Kuwait University, Faculty of Administrative Science Quantitative Methods and Information Systems Department P.O. Box 5969 Safat, code no. 13060 Kuwait ABSTRACT In this paper, we present an enhanced rough set approach for attribute reduction and generating classification rules from digital mammogram datasets. For this purpose, the presented approach is described in a hierarchical fashion. First, the preprocessing phase is adopted to enhance the contrast and edges of the mammogram images; moreover image processing segmentation algorithm is used to extract the region of interest. In the next phase, five texture features from the co-occurrence matrix are extracted and represented in attribute vector, and the reducts with minimal number of attributes are extracted. In the third phase, the decision rules within the generated reduct sets are generated. In the last phase, the classifier model was built and quadratic distances similarly function is used for matching process. To evaluate the validity of the rules based on the approximation quality of the attributes, we introduce a statistical test to evaluate the significance of the rules. The experimental results show that the classification algorithm performs well, reaching over 93% in accuracy with less number of rules compared with a well-known decision trees and neural network classifier models. KEYWORDS feature extraction and reduction, rule generation, similarity measure, texture feature extraction, gray-level co-occurrence matrices, decision trees E-mail addresses: Abo@.cba.edu.kw & iafar@cba.edu.kw This work was supported by Kuwait University, Research Grant No. [IQ03/02] 151 Brought to you by | New York University Bobst Library Technical Services Authenticated Download Date | 6/18/15 10:18 AM