Evaluating Classifiers\u27 Optimal Performances Over a Range of Misclassification Costs by Using Cost-Sensitive Classification

We believe that using the classification accuracy is not enough to evaluate the performances of classification algorithms. It can be misleading due to overlooking an important element which is the cost if classification is inaccurate. Furthermore, the Receiver Operational Characteristic (ROC) is one of the most popular graphs used to evaluate classifiers performances. However, one of the biggest ROC’s shortcomings is the assumption of equal costs for all misclassified data. Therefore, our goal is to reduce the total cost of decision making by selecting the classifier that has the least total misclassification cost. Nevertheless, the exact misclassification cost is usually unknown and hard to determine. To overcome such hurdle, we classify the data against a range of error costs. Thus, we use the cost range and the operating classification threshold range to show any performance differences among classifiers