In breast cancer screening practice, radiologists compare multiple views during the interpretation of mammograms to detect breast cancers. Hence, it is natural that information derived from multiple mammograms can be used for computer-aided detection (CAD) system to obtain better sensitivity and/or specificity. However, similarity features derived from the combination of cranio-caudal (CC) and mediolateral oblique (MLO) views are weak for classifying masses, because a breast is elastic and deformable. In this study, therefore, a new mass classification with boosting algorithm is proposed, aiming to reduce FPs by combining the information of CC and MLO view mammograms. The proposed method has been developed under the following facts: (1) classifiers trained using similarity features are rather weak classifier; (2) boosting technique generates a single strong classifier by combining multiple weak classifiers. By combining the classifier ensemble framework with similarity features, we are able to improve mass classification performance in two-view analysis. In this study, 192 mammogram cases were collected from the public DDSM database (DB) to demonstrate the effectiveness of the proposed method in terms of improving mass classification. Results show that our proposed classifier ensemble method can improve an area under the ROC curve (AUC) of 0.7479, compared to the best single support vector machine (SVM) classifier using feature-level fusion (AUC of 0.7123). In addition, the weakness of similarity features is experimentally found to prove the feasibility of the proposed method.