Software development projects inevitably accumulate defects throughout the development process. Due to the high cost that defects can incur, careful consideration is crucial when predicting which sections of code are likely to contain defects. Classification algorithms used in machine learning can be used to create classifiers which can be used to predict defects. While traditional classification algorithms optimize for accuracy, cost-sensitive classification methods attempt to make predictions which incur the lowest classification cost. In this paper we propose a cost-sensitive classification technique called CSForest which is an ensemble of decision trees. We also propose a cost-sensitive voting technique called CSVoting in order to take advantage of the set of decision trees in minimizing the classification cost. We then investigate a potential solution to class imbalance within our decision forest algorithm. We empirically evaluate the proposed techniques comparing them with six (6) classifier algorithms on six (6) publicly available clean datasets that are commonly used in the research on software defect prediction. Our initial experimental results indicate a clear superiority of the proposed techniques over the existing ones.