Feature selection for neural networks using Parzen density estimator

Chulhee Lee, Jon A. Benediktsson, David A. Landgrebe

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

In this paper, a new feature selection method for neural networks is proposed using the Parzen density estimator. A new feature set is selected employing the recently published decision boundary feature selection algorithm. The selected feature set is then used to train a neural network. Using a reduced feature set, we attempt to reduce the training time of the neural network and obtain a simpler neural network, further reducing the classification time for test data. Experiments show promising results.

Original languageEnglish
Title of host publicationIGARSS 1992 - International Geoscience and Remote Sensing Symposium
Subtitle of host publicationInternational Space Year: Space Remote Sensing
EditorsRuby Williamson, Tammy Stein
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages839-841
Number of pages3
ISBN (Electronic)0780301382
DOIs
Publication statusPublished - 1992
Event12th Annual International Geoscience and Remote Sensing Symposium, IGARSS 1992 - Houston, United States
Duration: 1992 May 261992 May 29

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Volume2

Other

Other12th Annual International Geoscience and Remote Sensing Symposium, IGARSS 1992
CountryUnited States
CityHouston
Period92/5/2692/5/29

Bibliographical note

Funding Information:
'This work was sumrted in part by NASA Grant NAGW-925.

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Earth and Planetary Sciences(all)

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