A neural network approach to the classification problem

✍ Scribed by James W. Denton; Ming S. Hung; Barbara A. Osyk

• Publisher: Elsevier Science
• Year: 1990
• Tongue: English
• Weight: 612 KB
• Volume: 1
• Category: Article
• ISSN: 0957-4174
• DOI: 10.1016/0957-4174(90)90050-5

✦ Synopsis

The task of classifying observations into known groups is a common problem in decision making. A wealth of statistical approaches, commencing with Fisher's linear discriminant function, and including variations to accommodate a variety of modeling assumptions, have been proposed. In addition, nonparametric approaches based on various mathematical programming models have also been proposed as solutions. All of these proposed solutions have performed well when conditions favorable to the specific model are present. The modeler, therefore, can usually be assured of a good solution to his problem if he chooses a model which fits his situation. In this paper, the performance of a neural network as a classifier is evaluated. It is found that the performance of the neural network is comparable to the best of the other methods under a wide variety of modeling assumptions. The use of neural networks as classifiers thus relieves the modeler of testing assumptions which would otherwise be critical to the performance of the usual classification techniques.