Decision boundary feature extraction for neural networks

Chul Hee Lee, David A. Landgrebe

Research output: Contribution to journalArticle

99 Citations (Scopus)

Abstract

In this paper, we propose a new feature extraction method for feedforward neural networks. The method is based on the recently published decision boundary feature extraction algorithm which is based on the fact that all the necessary features for classification can be extracted from the decision boundary. The decision boundary feature extraction algorithm can take advantage of characteristics of neural networks which can solve complex problems with arbitrary decision boundaries without assuming underlying probability distribution functions of the data. To apply the decision boundary feature extraction method, we first give a specific definition for the decision boundary in a neural network. Then, we propose a procedure for extracting all the necessary features for classification from the decision boundary. Experiments show promising results.

Original languageEnglish
Pages (from-to)75-83
Number of pages9
JournalIEEE Transactions on Neural Networks
Volume8
Issue number1
Publication statusPublished - 1997 Dec 1

Fingerprint

Feature extraction
Neural networks
Feedforward neural networks
Probability distributions
Distribution functions
Experiments

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

Cite this

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Decision boundary feature extraction for neural networks. / Lee, Chul Hee; Landgrebe, David A.

In: IEEE Transactions on Neural Networks, Vol. 8, No. 1, 01.12.1997, p. 75-83.

Research output: Contribution to journalArticle

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