Algorithms for optimal dyadic decision trees
Abstract A dynamic programming 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 param...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Hush, Don [verfasserIn] |
|---|
| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2010 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s) 2010 |
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| Übergeordnetes Werk: |
Enthalten in: Machine learning - Springer US, 1986, 80(2010), 1 vom: 28. Jan., Seite 85-107 |
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| Übergeordnetes Werk: |
volume:80 ; year:2010 ; number:1 ; day:28 ; month:01 ; pages:85-107 |
| Links: |
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| DOI / URN: |
10.1007/s10994-010-5167-x |
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OLC2026522820 |
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10.1007/s10994-010-5167-x doi (DE-627)OLC2026522820 (DE-He213)s10994-010-5167-x-p DE-627 ger DE-627 rakwb eng 150 004 VZ Hush, Don verfasserin aut Algorithms for optimal dyadic decision trees 2010 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2010 Abstract A dynamic programming 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, replacing the dynamic programming algorithm with a memoized recursive algorithm whose 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. Decision tree Classification Learning algorithm Porter, Reid aut Enthalten in Machine learning Springer US, 1986 80(2010), 1 vom: 28. Jan., Seite 85-107 (DE-627)12920403X (DE-600)54638-0 (DE-576)014457377 0885-6125 nnns volume:80 year:2010 number:1 day:28 month:01 pages:85-107 https://doi.org/10.1007/s10994-010-5167-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_21 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_70 GBV_ILN_100 GBV_ILN_130 GBV_ILN_2006 GBV_ILN_2021 GBV_ILN_4012 GBV_ILN_4046 GBV_ILN_4307 GBV_ILN_4318 AR 80 2010 1 28 01 85-107 |
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10.1007/s10994-010-5167-x doi (DE-627)OLC2026522820 (DE-He213)s10994-010-5167-x-p DE-627 ger DE-627 rakwb eng 150 004 VZ Hush, Don verfasserin aut Algorithms for optimal dyadic decision trees 2010 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2010 Abstract A dynamic programming 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, replacing the dynamic programming algorithm with a memoized recursive algorithm whose 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. Decision tree Classification Learning algorithm Porter, Reid aut Enthalten in Machine learning Springer US, 1986 80(2010), 1 vom: 28. Jan., Seite 85-107 (DE-627)12920403X (DE-600)54638-0 (DE-576)014457377 0885-6125 nnns volume:80 year:2010 number:1 day:28 month:01 pages:85-107 https://doi.org/10.1007/s10994-010-5167-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_21 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_70 GBV_ILN_100 GBV_ILN_130 GBV_ILN_2006 GBV_ILN_2021 GBV_ILN_4012 GBV_ILN_4046 GBV_ILN_4307 GBV_ILN_4318 AR 80 2010 1 28 01 85-107 |
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10.1007/s10994-010-5167-x doi (DE-627)OLC2026522820 (DE-He213)s10994-010-5167-x-p DE-627 ger DE-627 rakwb eng 150 004 VZ Hush, Don verfasserin aut Algorithms for optimal dyadic decision trees 2010 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2010 Abstract A dynamic programming 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, replacing the dynamic programming algorithm with a memoized recursive algorithm whose 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. Decision tree Classification Learning algorithm Porter, Reid aut Enthalten in Machine learning Springer US, 1986 80(2010), 1 vom: 28. Jan., Seite 85-107 (DE-627)12920403X (DE-600)54638-0 (DE-576)014457377 0885-6125 nnns volume:80 year:2010 number:1 day:28 month:01 pages:85-107 https://doi.org/10.1007/s10994-010-5167-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_21 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_70 GBV_ILN_100 GBV_ILN_130 GBV_ILN_2006 GBV_ILN_2021 GBV_ILN_4012 GBV_ILN_4046 GBV_ILN_4307 GBV_ILN_4318 AR 80 2010 1 28 01 85-107 |
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Abstract A dynamic programming 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, replacing the dynamic programming algorithm with a memoized recursive algorithm whose 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. © The Author(s) 2010 |
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Abstract A dynamic programming 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, replacing the dynamic programming algorithm with a memoized recursive algorithm whose 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. © The Author(s) 2010 |
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Abstract A dynamic programming 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, replacing the dynamic programming algorithm with a memoized recursive algorithm whose 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. © The Author(s) 2010 |
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