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      • Department of Electrical and Electronics Engineering
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      Subset selection with structured dictionaries in classification

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      Author
      İnce, N. F.
      Göksu, F.
      Tewfik, A. H.
      Onaran, İbrahim
      Çetin, A. Enis
      Date
      2007
      Source Title
      Proceedings of the 15th European Signal Processing Conference, EUSIPCO 2007
      Print ISSN
      2219-5491
      Publisher
      EURASIP
      Pages
      1877 - 1881
      Language
      English
      Type
      Conference Paper
      Item Usage Stats
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      Abstract
      This paper describes a new approach for the selection of discriminant time-frequency features for classification. Unlike previous approaches that use the individual discrimination power of expansion coefficients, the proposed approach selects a subset of features by implementing a classifier directed pruning of an initial redundant set of candidate features. The candidate features are calculated from a structured redundant time-frequency analysis of the signal, such as an undecimated wavelet transform. We show that the proposed approach has a performance that is as good as or better than traditional classification approaches while using a much smaller number of features. In particular, we provide experimental results to demonstrate the superior performance of the algorithm in the area of impact acoustic classification for food kernel inspection. The proposed algorithm achieved 91.8% and 98.5% classification accuracies in separating open shell from closed shell pistachio nuts and discriminating between empty and full hazelnuts respectively. Traditional methods used in this area resulted in 82% and 97% classification accuracies respectively.
      Keywords
      Classification accuracy
      Classification approach
      Closed shells
      Expansion coefficients
      Food kernel inspection
      Impact acoustics
      Time frequency analysis
      Time frequency features
      Undecimated wavelet transform
      Permalink
      http://hdl.handle.net/11693/27000
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      • Department of Electrical and Electronics Engineering 3525
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