The effect of training data set size and the complexity of the separation function on neural network classification capability: The two-group case

Moshe Leshno, Yishay Spector

Research output: Contribution to journalArticlepeer-review

Abstract

Classification among groups is a crucial problem in managerial decision making. Classification techniques are used in: identifying stressed firms, classifying among consumer types, and rating of firms' bonds, etc. Neural networks are recognized as important and emerging methodologies in the area of classification. In this paper, we study the effect of training sample size and the neural network topology on the classification capability of neural networks. We also compare neural network capabilities with those of commonly used statistical methodologies. Experiments were designed and carried out on two-group classification problems to find answers to these questions. The prediction capability of the neural network models are better than traditional statistical models. The learning capability of the neural networks is improving compared to traditional models because the discriminate function is more complex. For real world classification problems, the usage of neural networks is highly recommended, for two reasons: learning capability and flexibility. Learning capability: Neural network classifies better in sterile experiments as performed in this research. Flexibility: Real life data are rarely not contaminated with noise, such as unknown distributions, and missing variables, etc. Neural networks differ from a statistical model that it is not dependent on any assumption concerning the data set distribution.

Original languageEnglish
Pages (from-to)699-717
Number of pages19
JournalNaval Research Logistics
Volume44
Issue number8
DOIs
StatePublished - Dec 1997
Externally publishedYes

Keywords

  • Classification
  • Neural networks
  • Simulation
  • Statistical techniques

Fingerprint

Dive into the research topics of 'The effect of training data set size and the complexity of the separation function on neural network classification capability: The two-group case'. Together they form a unique fingerprint.

Cite this