A Classification Approach Incorporating Misclassification Costs

Abstract

We explore an algorithm for learning a classification procedure to minimize the cost of misclassified examples. The described approach is based on the generation of oblique decision trees. The various misclassification costs are defined by a cost matrix. A special splitting criterion is defined to determine the next node for splitting. Clustering techniques are used to process the splitting. The specific splitting criterion is based on cost histograms that count the misclassification costs per class. To avoid overfitting cross-validation techniques are directly integrated into the training cycle to terminate the splitting process. Several successful tests with different data sets cause this method to seem very promising.