An investigation of neural network classifiers with unequal misclassification costs and group sizes

Despite a larger number of successful applications of artificial neural networks for classification in business and other areas, published research has not considered the effects of misclassification costs and group sizes. Without the consideration of uneven misclassification costs, the classifier development will be compromised in minimizing the total misclassification errors. The use of this simplified model will not only result in poor decision capability when misclassification errors are significantly unequal, but also increase the model bias in favor of larger groups. This paper explores the issues of asymmetric misclassification costs and imbalanced group sizes through an application of neural networks to thyroid disease diagnosis. The results show that both asymmetric misclassification costs and imbalanced group sizes have significant effects on the neural network classification performance. In addition, we find that increasing the sample size and resampling are two effective approaches to counteract the problems.