Using Decision Trees for Comparing Pattern Recognition Feature Sets

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Determination of the best set of features has been acknowledged as one of the most difficult tasks in the pattern recognition process. In this report significance tests on the sort-ordered, sample-size normalized vote distribution of an ensemble of decision trees is introduced as a method of evaluating relative quality of feature sets. Alternative functional forms for feature sets are also examined. Associated standard deviations provide the means to evaluate the effect of the number of folds, the number of classifiers per fold, and the sample size on the resulting classifications. The method is applied to a problem for which a ... continued below

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PDF-file: 26 pages; size: 0.8 Mbytes

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Proctor, D D August 18, 2005.

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Determination of the best set of features has been acknowledged as one of the most difficult tasks in the pattern recognition process. In this report significance tests on the sort-ordered, sample-size normalized vote distribution of an ensemble of decision trees is introduced as a method of evaluating relative quality of feature sets. Alternative functional forms for feature sets are also examined. Associated standard deviations provide the means to evaluate the effect of the number of folds, the number of classifiers per fold, and the sample size on the resulting classifications. The method is applied to a problem for which a significant portion of the training set cannot be classified unambiguously.

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PDF-file: 26 pages; size: 0.8 Mbytes

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  • Journal Name: Astrophysical Journal Supplement Series, vol. 165, N/A, July 1, 2006, pp. 95-107; Journal Volume: 165

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  • Report No.: UCRL-JRNL-214691-DRAFT
  • Grant Number: W-7405-ENG-48
  • Office of Scientific & Technical Information Report Number: 900441
  • Archival Resource Key: ark:/67531/metadc886615

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  • August 18, 2005

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  • Sept. 22, 2016, 2:13 a.m.

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  • Dec. 2, 2016, 5:58 p.m.

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Proctor, D D. Using Decision Trees for Comparing Pattern Recognition Feature Sets, article, August 18, 2005; Livermore, California. (digital.library.unt.edu/ark:/67531/metadc886615/: accessed December 17, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.