Algorithms for optimal dyadic decision trees

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A new algorithm for constructing optimal dyadic decision trees was recently introduced, analyzed, and shown to be very effective for low dimensional data sets. This paper enhances and extends this algorithm by: introducing an adaptive grid search for the regularization parameter that guarantees optimal solutions for all relevant trees sizes, revising the core tree-building algorithm so that its run time is substantially smaller for most regularization parameter values on the grid, and incorporating new data structures and data pre-processing steps that provide significant run time enhancement in practice.

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Hush, Don & Porter, Reid January 1, 2009.

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A new algorithm for constructing optimal dyadic decision trees was recently introduced, analyzed, and shown to be very effective for low dimensional data sets. This paper enhances and extends this algorithm by: introducing an adaptive grid search for the regularization parameter that guarantees optimal solutions for all relevant trees sizes, revising the core tree-building algorithm so that its run time is substantially smaller for most regularization parameter values on the grid, and incorporating new data structures and data pre-processing steps that provide significant run time enhancement in practice.

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  • Journal Name: Machine Learning

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  • Report No.: LA-UR-09-00704
  • Report No.: LA-UR-09-704
  • Grant Number: AC52-06NA25396
  • Office of Scientific & Technical Information Report Number: 956380
  • Archival Resource Key: ark:/67531/metadc935369

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  • January 1, 2009

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  • Nov. 13, 2016, 7:26 p.m.

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  • Dec. 9, 2016, 10:35 p.m.

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Hush, Don & Porter, Reid. Algorithms for optimal dyadic decision trees, article, January 1, 2009; [New Mexico]. (digital.library.unt.edu/ark:/67531/metadc935369/: accessed August 14, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.