Cost sensitive decision forest and voting for software defect prediction

While traditional classification algorithms optimize for accuracy, cost-sensitive classification methods attempt to make predictions that produce 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. The proposed techniques are empirically evaluated by comparing them with five (5) 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.