Optimized Tumor Breast Cancer Classification Using Combining Random Subspace and Static Classifiers Selection Paradigms

Breast cancer is the most frequently diagnosed cancer in women worldwide and the leading cause of cancer death among females. Currently the most effective method for early detection and screening of breast abnormalities is mammography. Computer aided design (CAD) systems are used to assist radiologists in better classification of tumor in a mammography as benign or malignant. Ensemble classifier construction has received considerable attention in the recent years. In the modeling of classifier ensemble, many researchers believe that the success of classifier ensembles only when classifier members present diversity among themselves. The most widely used ensemble creation techniques are focused on incorporating the concept of diversity with the construction of different features subsets or selection of the most diverse components from initial classifiers pool. Therefore the motivation of this work is to propose a CAD system using a novel classification approach based on feature selection and static classifier selection schemes.