Paper Info
Title
An investigation of neural network classifiers with unequal misclassification costs and group sizes
Abstract
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.
Year
DOI
Venue
2010
10.1016/j.dss.2009.11.008
Decision Support Systems
Keywords
Field
DocType
neural network,neural networks,neural network classifier,misclassification error,total misclassification error,larger group,imbalanced group size,group size,group sizes,misclassification cost,uneven misclassification cost,artificial neural network,medical diagnosis,misclassification costs,unequal misclassification cost,asymmetric misclassification cost,sample size
Data mining,Neural network classification,Computer science,Artificial neural network,Classifier (linguistics),Resampling,Medical diagnosis,Sample size determination
Journal
Volume
Issue
ISSN
48
4
Decision Support Systems
Citations 
PageRank 
References 
11
0.54
21
Authors
4
Name
Order
Citations
PageRank
Jyh-shyan Lan1120.89
Michael Y. Hu242655.74
B. Eddy Patuwo326119.98
G. Peter Zhang480251.61