Decision Sciences Journal
Volume 30, Number 3
Summer 1999
The Effect of Misclassification Costs on Neural Network Classifiers
Victor L. Berardi
Department of Management, College of Business, Bloomsburg University,
Bloomsburg, PA 17815, email: vberardi@planetx.bloomu.edu
G. Peter Zhang
Department of Decision Sciences, J. Mack Robinson College of
Business,
Georgia State University, Atlanta, GA 30303, email: gpzhang@gsu.edu
ABSTRACT. The potential of neural networks for classification
problems has been established by numerous successful applications
reported in the literature. One of the major assumptions used
in almost all studies is the equal cost consequence of misclassification.
With this assumption, minimizing the total number of misclassification
errors is the sole objective in developing a neural network classifier.
Often this is done simply to ease model development and the selection
of classification decision points. However, it is not appropriate
for many real situations such as quality assurance, direct marketing,
bankruptcy prediction, and medical diagnosis where misclassification
costs have unequal consequences for different categories. In
this paper, we investigate the issue of unequal misclassification
costs in neural network classifiers. Through an application in
thyroid disease diagnosis, we find that different cost considerations
have significant effects on the classification performance and
that appropriate use of cost information can aid in optimal decision
making. A cross-validation technique is employed to alleviate
the problem of bias in the training set and to examine the robustness
of neural network classifiers with regard to sampling variations
and cost differences.
Subject Areas: Classification, Medical Diagnosis, Misclassification
Costs, Neural Networks, and Statistical Techniques. |