Statistical issues in machine learning: towards reliable split selection and variable importance measures
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis Book |
| Language: | English |
| Published: |
Göttingen
Cuvillier
2008
|
| Edition: | 1. Aufl. |
| Subjects: | |
| Online Access: | Volltext https://nbn-resolving.org/urn:nbn:de:bvb:19-89043 Inhaltsverzeichnis |
| Physical Description: | XII, 179 S. graph. Darst. |
| ISBN: | 9783867276610 |
Staff View
MARC
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| 245 | 1 | 0 | |a Statistical issues in machine learning |b towards reliable split selection and variable importance measures |c von Carolin Strobl |
| 250 | |a 1. Aufl. | ||
| 264 | 1 | |a Göttingen |b Cuvillier |c 2008 | |
| 300 | |a XII, 179 S. |b graph. Darst. | ||
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Record in the Search Index
| _version_ | 1804137984395575296 |
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| adam_text | CONTENTS SCOPE OF THIS WORK VI 1. INTRODUCTION 1 1.1 CLASSIFICATION
TREES 5 1.1.1 SPLIT SELECTION AND STOPPING MIES 5 1.1.2 PREDICTION AND
INTERPRETATION 10 1.1.3 VARIABLE SELECTION BIAS AND INSTABILITY 13 1.2
ROBUST CLASSISCATION TREES AND ENSEMBLE METHODS 16 1.3 CHARACTERISTICS
AND CAVEATS 19 1.3.1 SMALL N LARGE P APPLICABILITY 19 1.3.2 OUT-OF-BAG
ERROR ESTIMATION 21 1.3.3 MISSING VALUE HANDLING 22 1.3.4 RANDOMNESS AND
STABILITY 22 2. VARIABLE SELECTION BIAS IN CLASSIFICATION TREES 25 2.1
ENTROPY ESTIMATION 28 2.1.1 BINARY SPLITTING 28 GESCANNT DURCH
BIBLIOGRAFISCHE INFORMATIONEN HTTP://D-NB.INFO/990426564 DIGITALISIERT
DURCH 4.2.2 EFFECTS IN CLASSIFICATION TREES BASED ON IMPRECISE
PROBABILITIES .... 65 CONTENTS 2.1.2 JT-ARY SPLITTING 32 2.2 MULTIPLE
COMPARISONS IN CUTPOINT SELECTION 34 2.3 SUMMARY 35 3. EVALUATION OF AN
UNBIASED VARIABLE SELECTION CRITERION 37 3.1 OPTIMALLY SELECTED
STATISTICS 38 3.2 SIMULATION STUDIES 40 3.2.1 NULL CASE 41 3.2.2 POWER
CASE 1 42 3.2.3 POWER CASE II 43 3.3 APPLICATION TO VETERINARY DATA 46
3.3.1 VARIABLE SELECTION RANKING 47 3.3.2 SELECTED SPLITTING VARIABLES
47 3.4 SUMMARY 48 4. ROBUST AND UNBIASED VARIABLE SELECTION IN K-ARY
SPLITTING 54 4.1 CLASSIFICATION TREES BASED ON IMPRECISE PROBABILITIES
55 4.1.1 TOTAL IMPURITY CRITERIA 57 4.1.2 SPLIT SELECTION PROCEDURE 59
4.1.3 CHARACTERISTICS OF THE TOTAL IMPURITY CRITERION TU2 60 4.2
EMPIRICAL ENTROPY MEASURES IN SPLIT SELECTION 64 4.2.1 ESTIMATION BIAS
FOR THE EMPIRICAL SHANNON ENTROPY 64 6.2.1 NULL CASE 105 CONTENTS III
4.2.3 SUGGESTED CORRECTIONS BASED ON THE IDM 67 4.3 SIMULATION STUDY 68
4.4 SUMMARY 69 5. ADAPTIVE CUTPOINT SELECTION IN TWIX ENSEMBLES 77 5.1
BUILDING TWIX ENSEMBLES 79 5.1.1 INSTABILITY OF CUTPOINT SELECTION IN
RECURSIVE PARTITIONING 80 5.1.2 SELECTING EXTRA CUTPOINTS 81 5.2 A NEW,
ADAPTIVE CRITERION FOR SELECTING EXTRA CUTPOINTS 83 5.2.1 ADDING VIRTUAL
OBSERVATIONS 84 5.2.2 RECOMPUTATION OF THE SPLIT CRITERION 85 5.3
BEHAVIOR OF THE ADAPTIVE CRITERION 88 5.3.1 APPLICATION TO OLIVES DATA
89 5.3.2 SIMULATION STUDY 91 5.4 OUTLOOK ON CREDAL PREDICTION AND
AGGREGATION SCHEMES 93 5.4.1 CREDAL PREDICTION RULES 93 5.4.2
AGGREGATION SCHEINES 96 5.5 SUMMARY 97 6. UNBIASED VARIABLE IMPORTANCE
IN RANDOM FORESTS AND BAGGING 99 6.1 RANDOM FOREST VARIABLE IMPORTANCE
MEASURES 100 6.2 SIMULATION STUDIES 102 8.4 SUMMARY 158 IV CONTENTS
6.2.2 POWER CASE 107 6.3 SOURCES OF VARIABLE IMPORTANCE BIAS 111 6.3.1
VARIABLE SELECTION BIAS IN INDIVIDUAL CLASSIFICATION TREES 112 6.3.2
EFFECTS INDUCED BY BOOTSTRAPPING 113 6.4 APPLICATION TO C-TO-U
CONVERSION DATA 115 6.5 SUMMARY 118 7. STATISTICAL PROPERTIES OF BREIMAN
AND CUTLER S TEST 130 7.1 INVESTIGATING THE CURRENT TEST 131 7.1.1 THE
POWER 131 7.1.2 THE EONSTRUCTION OF THE Z-SCORE 133 7.1.3 SPECIFYING THE
NULL HYPOTHESIS 134 7.2 SUMMARY 135 8. CONDITIONAL VARIABLE IMPORTANCE
138 8.1 VARIABLE SELECTION IN RANDOM FORESTS 143 8.1.1 SIMULATION DESIGN
144 8.1.2 ILLUSTRATION OF VARIABLE SELECTION 145 8.2 A SECOND LOOK AT
THE PERMUTATION IMPORTANCE 147 8.2.1 BACKGROUND: TYPES OF INDEPENDENCE
147 8.2.2 A NEW, CONDITIONAL PERMUTATION SCHEME 150 8.2.3 SIMULATION
RESULTS 153 8.3 APPLICATION TO PEPTIDE-BINDING DATA 156 CONTENTS 9.
CONCLUSION AND OUTLOOK 159 BIBLIOGRAPHY 165
|
| adam_txt |
CONTENTS SCOPE OF THIS WORK VI 1. INTRODUCTION 1 1.1 CLASSIFICATION
TREES 5 1.1.1 SPLIT SELECTION AND STOPPING MIES 5 1.1.2 PREDICTION AND
INTERPRETATION 10 1.1.3 VARIABLE SELECTION BIAS AND INSTABILITY 13 1.2
ROBUST CLASSISCATION TREES AND ENSEMBLE METHODS 16 1.3 CHARACTERISTICS
AND CAVEATS 19 1.3.1 "SMALL N LARGE P" APPLICABILITY 19 1.3.2 OUT-OF-BAG
ERROR ESTIMATION 21 1.3.3 MISSING VALUE HANDLING 22 1.3.4 RANDOMNESS AND
STABILITY 22 2. VARIABLE SELECTION BIAS IN CLASSIFICATION TREES 25 2.1
ENTROPY ESTIMATION 28 2.1.1 BINARY SPLITTING 28 GESCANNT DURCH
BIBLIOGRAFISCHE INFORMATIONEN HTTP://D-NB.INFO/990426564 DIGITALISIERT
DURCH 4.2.2 EFFECTS IN CLASSIFICATION TREES BASED ON IMPRECISE
PROBABILITIES . 65 CONTENTS 2.1.2 JT-ARY SPLITTING 32 2.2 MULTIPLE
COMPARISONS IN CUTPOINT SELECTION 34 2.3 SUMMARY 35 3. EVALUATION OF AN
UNBIASED VARIABLE SELECTION CRITERION 37 3.1 OPTIMALLY SELECTED
STATISTICS 38 3.2 SIMULATION STUDIES 40 3.2.1 NULL CASE 41 3.2.2 POWER
CASE 1 42 3.2.3 POWER CASE II 43 3.3 APPLICATION TO VETERINARY DATA 46
3.3.1 VARIABLE SELECTION RANKING 47 3.3.2 SELECTED SPLITTING VARIABLES
47 3.4 SUMMARY 48 4. ROBUST AND UNBIASED VARIABLE SELECTION IN K-ARY
SPLITTING 54 4.1 CLASSIFICATION TREES BASED ON IMPRECISE PROBABILITIES
55 4.1.1 TOTAL IMPURITY CRITERIA 57 4.1.2 SPLIT SELECTION PROCEDURE 59
4.1.3 CHARACTERISTICS OF THE TOTAL IMPURITY CRITERION TU2 60 4.2
EMPIRICAL ENTROPY MEASURES IN SPLIT SELECTION 64 4.2.1 ESTIMATION BIAS
FOR THE EMPIRICAL SHANNON ENTROPY 64 6.2.1 NULL CASE 105 CONTENTS III
4.2.3 SUGGESTED CORRECTIONS BASED ON THE IDM 67 4.3 SIMULATION STUDY 68
4.4 SUMMARY 69 5. ADAPTIVE CUTPOINT SELECTION IN TWIX ENSEMBLES 77 5.1
BUILDING TWIX ENSEMBLES 79 5.1.1 INSTABILITY OF CUTPOINT SELECTION IN
RECURSIVE PARTITIONING 80 5.1.2 SELECTING EXTRA CUTPOINTS 81 5.2 A NEW,
ADAPTIVE CRITERION FOR SELECTING EXTRA CUTPOINTS 83 5.2.1 ADDING VIRTUAL
OBSERVATIONS 84 5.2.2 RECOMPUTATION OF THE SPLIT CRITERION 85 5.3
BEHAVIOR OF THE ADAPTIVE CRITERION 88 5.3.1 APPLICATION TO OLIVES DATA
89 5.3.2 SIMULATION STUDY 91 5.4 OUTLOOK ON CREDAL PREDICTION AND
AGGREGATION SCHEMES 93 5.4.1 CREDAL PREDICTION RULES 93 5.4.2
AGGREGATION SCHEINES 96 5.5 SUMMARY 97 6. UNBIASED VARIABLE IMPORTANCE
IN RANDOM FORESTS AND BAGGING 99 6.1 RANDOM FOREST VARIABLE IMPORTANCE
MEASURES 100 6.2 SIMULATION STUDIES 102 8.4 SUMMARY 158 IV CONTENTS
6.2.2 POWER CASE 107 6.3 SOURCES OF VARIABLE IMPORTANCE BIAS 111 6.3.1
VARIABLE SELECTION BIAS IN INDIVIDUAL CLASSIFICATION TREES 112 6.3.2
EFFECTS INDUCED BY BOOTSTRAPPING 113 6.4 APPLICATION TO C-TO-U
CONVERSION DATA 115 6.5 SUMMARY 118 7. STATISTICAL PROPERTIES OF BREIMAN
AND CUTLER'S TEST 130 7.1 INVESTIGATING THE CURRENT TEST 131 7.1.1 THE
POWER 131 7.1.2 THE EONSTRUCTION OF THE Z-SCORE 133 7.1.3 SPECIFYING THE
NULL HYPOTHESIS 134 7.2 SUMMARY 135 8. CONDITIONAL VARIABLE IMPORTANCE
138 8.1 VARIABLE SELECTION IN RANDOM FORESTS 143 8.1.1 SIMULATION DESIGN
144 8.1.2 ILLUSTRATION OF VARIABLE SELECTION 145 8.2 A SECOND LOOK AT
THE PERMUTATION IMPORTANCE 147 8.2.1 BACKGROUND: TYPES OF INDEPENDENCE
147 8.2.2 A NEW, CONDITIONAL PERMUTATION SCHEME 150 8.2.3 SIMULATION
RESULTS 153 8.3 APPLICATION TO PEPTIDE-BINDING DATA 156 CONTENTS 9.
CONCLUSION AND OUTLOOK 159 BIBLIOGRAPHY 165 |
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| isbn | 9783867276610 |
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| physical | XII, 179 S. graph. Darst. |
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| spelling | Strobl, Carolin 1978- Verfasser (DE-588)136184650 aut Statistical issues in machine learning towards reliable split selection and variable importance measures von Carolin Strobl 1. Aufl. Göttingen Cuvillier 2008 XII, 179 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Zugl.: München, Univ., Diss., 2008 Maschinelles Lernen - Automatische Klassifikation - Entscheidungsbaum - Bootstrap-Statistik - Stichprobe Automatische Klassifikation (DE-588)4120957-6 gnd rswk-swf Stichprobe (DE-588)4057502-0 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Bootstrap-Statistik (DE-588)4139168-8 gnd rswk-swf Entscheidungsbaum (DE-588)4347788-4 gnd rswk-swf (DE-588)4113937-9 Hochschulschrift gnd-content Maschinelles Lernen (DE-588)4193754-5 s Automatische Klassifikation (DE-588)4120957-6 s Entscheidungsbaum (DE-588)4347788-4 s Bootstrap-Statistik (DE-588)4139168-8 s Stichprobe (DE-588)4057502-0 s DE-604 Erscheint auch als Online-Ausgabe urn:nbn:de:bvb:19-89043 http://edoc.ub.uni-muenchen.de/8904/ Verlag kostenfrei Volltext https://nbn-resolving.org/urn:nbn:de:bvb:19-89043 Resolving-System DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016715774&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Strobl, Carolin 1978- Statistical issues in machine learning towards reliable split selection and variable importance measures Maschinelles Lernen - Automatische Klassifikation - Entscheidungsbaum - Bootstrap-Statistik - Stichprobe Automatische Klassifikation (DE-588)4120957-6 gnd Stichprobe (DE-588)4057502-0 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Bootstrap-Statistik (DE-588)4139168-8 gnd Entscheidungsbaum (DE-588)4347788-4 gnd |
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| title | Statistical issues in machine learning towards reliable split selection and variable importance measures |
| title_auth | Statistical issues in machine learning towards reliable split selection and variable importance measures |
| title_exact_search | Statistical issues in machine learning towards reliable split selection and variable importance measures |
| title_exact_search_txtP | Statistical issues in machine learning towards reliable split selection and variable importance measures |
| title_full | Statistical issues in machine learning towards reliable split selection and variable importance measures von Carolin Strobl |
| title_fullStr | Statistical issues in machine learning towards reliable split selection and variable importance measures von Carolin Strobl |
| title_full_unstemmed | Statistical issues in machine learning towards reliable split selection and variable importance measures von Carolin Strobl |
| title_short | Statistical issues in machine learning |
| title_sort | statistical issues in machine learning towards reliable split selection and variable importance measures |
| title_sub | towards reliable split selection and variable importance measures |
| topic | Maschinelles Lernen - Automatische Klassifikation - Entscheidungsbaum - Bootstrap-Statistik - Stichprobe Automatische Klassifikation (DE-588)4120957-6 gnd Stichprobe (DE-588)4057502-0 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Bootstrap-Statistik (DE-588)4139168-8 gnd Entscheidungsbaum (DE-588)4347788-4 gnd |
| topic_facet | Maschinelles Lernen - Automatische Klassifikation - Entscheidungsbaum - Bootstrap-Statistik - Stichprobe Automatische Klassifikation Stichprobe Maschinelles Lernen Bootstrap-Statistik Entscheidungsbaum Hochschulschrift |
| url | http://edoc.ub.uni-muenchen.de/8904/ https://nbn-resolving.org/urn:nbn:de:bvb:19-89043 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016715774&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT stroblcarolin statisticalissuesinmachinelearningtowardsreliablesplitselectionandvariableimportancemeasures |