Feature Selection Method for Neural Network for the Classification of Wood Veneer Defects

MS Packianather, PR Drake, Duc Pham

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


In this paper, a statistical approach based feature selection method for multilayered feedforward neural network for the classification of wood veneer defects is presented. This method focuses on identifying the superfluous input features by defining a Feature Rejection Criteria (FRC). It is based on an analysis of the intra-class and inter-class variation in the features and their correlation within the same class. The initial neural network design uses seventeen features of the acquired image of the wood veneer as inputs and classifies the veneer as clear wood or one of twelve possible defects (thirteen classes). The revised smaller eleven input neural network results in an improvement in the classification accuracy and time.
Original languageEnglish
Title of host publicationAutomation Congress Proceedings, 2008
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages6
ISBN (Print)9781889335384
Publication statusPublished - 1 Jan 2008
EventWorld Automation Congress (WAC), 2008 - , United Kingdom
Duration: 28 Sept 20082 Oct 2008


ConferenceWorld Automation Congress (WAC), 2008
Country/TerritoryUnited Kingdom


  • feature selection
  • Multilayered feedforward neural network
  • automatic visual inspection
  • wood veneer inspection
  • image classification


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