Automating the design of data mining algorithms: an evolutionary computation approach
Gespeichert in:
| Beteiligte Personen: | , |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Berlin [u.a.]
Springer
2010
|
| Schriftenreihe: | Natural computing series
|
| Schlagwörter: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020195452&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Beschreibung: | Literaturangaben |
| Umfang: | XIII, 187 S. graph. Darst. |
| ISBN: | 9783642025402 |
Internformat
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| 245 | 1 | 0 | |a Automating the design of data mining algorithms |b an evolutionary computation approach |c Gisele L. Pappa ; Alex A. Freitas |
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Datensatz im Suchindex
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|---|---|
| adam_text | CONTENTS INTRODUCTION 1 1.1 RULE INDUCTION ALGORITHMS 2 1.2 EVOLUTIONARY
COMPUTATION 4 1.2.1 GENETIC PROGRAMMING 4 1.3 THE MOTIVATION FOR
AUTOMATING THE DESIGN OF CLASSIFICATION ALGORITHMS 7 1.3.1 THE PROBLEM
OF THE SELECTIVE SUPERIORITY OF CLASSIFICATION ALGORITHMS 7 1.3.2 HUMAN
BIASES IN MANUALLY DESIGNED ALGORITHMS 10 1.3.3 A NEW LEVEL OF
AUTOMATION IN DATA MINING 11 1.4 OVERVIEW OF THE PROPOSED GENETIC
PROGRAMMING SYSTEM 12 REFERENCES 15 DATA MINING 17 2.1 INTRODUCTION 17
2.2 THE CLASSIFICATION TASK OF DATA MINING 18 2.2.1 ON PREDICTIVE
ACCURACY 19 2.2.2 ON OVERFITTING AND UNDERFILLING 22 2.2.3 ON THE
COMPREHENSIBILITY OF DISCOVERED KNOWLEDGE 23 2.3 DECISION TREE INDUCTION
25 2.4 RULE INDUCTION VIA THE SEQUENTIAL COVERING APPROACH 27 2.4.1
REPRESENTATION OF THE CANDIDATE RULES 30 2.4.2 SEARCH MECHANISM 32 2.4.3
RULE EVALUATION 34 2.4.4 RULE PRUNING METHODS 37 2.5 META-LEARNING 39
2.5.1 META-LEARNING FOR CLASSIFICATION ALGORITHM SELECTION 39 2.5.2
STACKED GENERALIZATION: META-LEARNING VIA A COMBINATION OF BASE
LEARNERS PREDICTIONS 42 2.6 SUMMARY 42 REFERENCES 43 BIBLIOGRAFISCHE
INFORMATIONEN HTTP://D-NB.INFO/997282002 DIGITALISIERT DURCH CONTENTS
EVOLUTIONARY ALGORITHMS 47 3.1 INTRODUCTION 47 3.2 AN OVERVIEW OF
EVOLUTIONARY ALGORITHMS 48 3.2.1 INDIVIDUAL REPRESENTATION 48 3.2.2
FITNESS FUNCTION 49 3.2.3 INDIVIDUAL SELECTION 50 3.2.4 GENETIC
OPERATORS 50 3.3 MULTIOBJECTIVE OPTIMIZATION 52 3.3.1 THE PARETE
OPTIMALITY CONCEPT 53 3.3.2 LEXICOGRAPHIC MULTIOBJECTIVE OPTIMIZATION 54
3.4 GENETIC PROGRAMMING VERSUS GENETIC ALGORITHMS: A CRITICAL
PERSPECTIVE 55 3.5 GENETIC PROGRAMMING 59 3.5.1 TERMINAL AND FUNCTION
SETS AND THE CLOSURE PROPERTY 62 3.5.2 FITNESS FUNCTION: AN EXAMPLE
INVOLVING REGRESSION 64 3.5.3 SELECTION AND GENETIC OPERATORS 65 3.5.4
APPROACHES FOR SATISFYING THE CLOSURE PROPERTY 68 3.5.5 BLOAT 68 3.6
GRAMMAR-BASED GENETIC PROGRAMMING 70 3.6.1 GRAMMARS 72 3.6.2 GGP WITH
SOLUTION-ENCODING INDIVIDUAL 74 3.6.3 GGP WITH
PRODUCTION-RULE-SEQUENCE-ENCODING INDIVIDUAL . 77 3.7 SUMMARY 80
REFERENCES 80 GENETIC PROGRAMMING FOR CLASSIFICATION AND ALGORITHM
DESIGN 85 4.1 INTRODUCTION 85 4.2 CLASSIFICATION MODELS VERSUS
CLASSIFICATION ALGORITHMS 86 4.3 GENETIC PROGRAMMING FOR EVOLVING
CLASSIFICATION MODELS 88 4.3.1 EVOLVING CLASSIFICATION FUNCTIONS OR
CLASSIFICATION RULES... 89 4.3.2 EVOLVING DECISION TREES 91 4.4 GENETIC
PROGRAMMING FOR EVOLVING COMPONENTS OF RULE INDUCTION ALGORITHMS 92 4.5
GENETIC PROGRAMMING FOR EVOLVING CLASSIFICATION SYSTEMS 95 4. CONTENTS
AUTOMATING THE DESIGN OF RULE INDUCTION ALGORITHMS 109 5.1 INTRODUCTION
109 5.2 THE GRAMMAR: SPECIFYING THE BUILDING BLOCKS OF RULE INDUCTION
ALGORITHMS ILL 5.2.1 THE NEW RULE INDUCTION ALGORITHMIC COMPONENTS IN
THE GRAMMAR 116 5.3 INDIVIDUAL REPRESENTATION 117 5.4 POPULATION
INITIALIZATION 118 5.5 INDIVIDUAL EVALUATION 121 5.5.1 FROM A DERIVATION
TREE TO JAVA CODE 124 5.5.2 SINGLE-OBJECTIVE FITNESS 126 5.5.3
MULTIOBJECTIVE FITNESS 129 5.6 CROSSOVER AND MUTATION OPERATIONS 131 5.7
SUMMARY 133 REFERENCES 133 COMPUTATIONAL RESULTS ON THE AUTOMATIC DESIGN
OF FULL RULE INDUCTION ALGORITHMS 137 6. 1 INTRODUCTION 137 6.2 EVOLVING
RULE INDUCTION ALGORITHMS ROBUST ACROSS DIFFERENT APPLICATION DOMAINS
138 6.2.1 INVESTIGATING THE GGP SYSTEM S SENSITIVITY TO PARAMETERS ..
139 6.2.2 COMPARING GGP-DESIGNED RULE INDUCTION ALGORITHMS WITH
HUMAN-DESIGNED RULE INDUCTION ALGORITHMS 142 6.2.3 TO WHAT EXTENT ARE
GGP-RIS DIFFERENT FROM MANUALLY DESIGNED RULE INDUCTION ALGORITHMS? 144
6.2.4 META-TRAINING SET VARIATIONS 148 6.2.5 GGP SYSTEM S GRAMMAR
VARIATIONS 151 6.2.6 GGP VERSUS GRAMMAR-BASED HILL-CLIMBING SEARCH 153
6.2.7 MOGGP: A MULTIOBJECTIVE VERSION OF THE PROPOSED GGP .. 156 6.2.8 A
NOTE ON THE GGP SYSTEM S EXECUTION TIME 160 6.3 EVOLVING RULE INDUCTION
ALGORITHMS TAILORED TO THE TARGET APPLICATIO XII CONTENTS 7.2 DESIGNING
RULE INDUCTION ALGORITHMS TAILORED TO A TYPE OF DATASET 180 7.3
INVESTIGATING OTHER TYPES OF SEARCH METHODS FOR AUTOMATED ALGORITHM
DESIGN 181 7.4 AUTOMATICALLY DESIGNING OTHER TYPES OF CLASSIFICATION
ALGORITHMS. 182 7.5 AUTOMATICALLY DESIGNING OTHER TYPES OF DATA MINING
ALGORITHMS . 183 REFERENCES 184 INDEX 185
|
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| author | Pappa, Gisele L. Freitas, Alex A. |
| author_facet | Pappa, Gisele L. Freitas, Alex A. |
| author_role | aut aut |
| author_sort | Pappa, Gisele L. |
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| bvnumber | BV025599908 |
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| ctrlnum | (OCoLC)488693786 (DE-599)BVBBV025599908 |
| dewey-full | 006.312 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.312 |
| dewey-search | 006.312 |
| dewey-sort | 16.312 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Book |
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| id | DE-604.BV025599908 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T14:31:08Z |
| institution | BVB |
| isbn | 9783642025402 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-020195452 |
| oclc_num | 488693786 |
| open_access_boolean | |
| owner | DE-11 |
| owner_facet | DE-11 |
| physical | XIII, 187 S. graph. Darst. |
| publishDate | 2010 |
| publishDateSearch | 2010 |
| publishDateSort | 2010 |
| publisher | Springer |
| record_format | marc |
| series2 | Natural computing series |
| spellingShingle | Pappa, Gisele L. Freitas, Alex A. Automating the design of data mining algorithms an evolutionary computation approach Data Mining (DE-588)4428654-5 gnd Computer (DE-588)4070083-5 gnd Evolutionärer Algorithmus (DE-588)4366912-8 gnd Algorithmus (DE-588)4001183-5 gnd |
| subject_GND | (DE-588)4428654-5 (DE-588)4070083-5 (DE-588)4366912-8 (DE-588)4001183-5 |
| title | Automating the design of data mining algorithms an evolutionary computation approach |
| title_auth | Automating the design of data mining algorithms an evolutionary computation approach |
| title_exact_search | Automating the design of data mining algorithms an evolutionary computation approach |
| title_full | Automating the design of data mining algorithms an evolutionary computation approach Gisele L. Pappa ; Alex A. Freitas |
| title_fullStr | Automating the design of data mining algorithms an evolutionary computation approach Gisele L. Pappa ; Alex A. Freitas |
| title_full_unstemmed | Automating the design of data mining algorithms an evolutionary computation approach Gisele L. Pappa ; Alex A. Freitas |
| title_short | Automating the design of data mining algorithms |
| title_sort | automating the design of data mining algorithms an evolutionary computation approach |
| title_sub | an evolutionary computation approach |
| topic | Data Mining (DE-588)4428654-5 gnd Computer (DE-588)4070083-5 gnd Evolutionärer Algorithmus (DE-588)4366912-8 gnd Algorithmus (DE-588)4001183-5 gnd |
| topic_facet | Data Mining Computer Evolutionärer Algorithmus Algorithmus |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020195452&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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