Accurate quantification of coronary artery calcium provides an opportunity to assess the extent of atherosclerosis disease. Coronary calcification burden has been reported to be associated with cardiovascular risk. Currently, an observer has to identify the coronary calcifications among a set of candidate regions, obtained by thresholding and connected component labeling, by clicking on them. To relieve the observer of such a labor-intensive task, an automated tool is needed that can detect and quantify the coronary calcifications. However, the diverse and heterogeneous nature of the candidate regions poses a significant challenge. In this paper, we investigate a supervised classification-based approach to distinguish the coronary calcifications from all the candidate regions and propose a two-stage, hierarchical classifier for automated coronary calcium detection. At each stage, we learn an ensemble of classifiers where each classifier is a cost-sensitive learner trained on a distinct asymmetrically sampled data subset. We compute the relative location of the calcifications with respect to a heart-centered coordinate system, and also use the neighboring regions of the calcifications to better characterize their properties for discrimination. Our method detected coronary calcifications with an accuracy, sensitivity and specificity of 98.27, 92.07 and 98.62%, respectively, for a testing dataset of non-contrast computed tomography scans from 105 subjects.