Metalearning : : Applications to Automated Machine Learning and Data Mining.
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Superior document: | Cognitive Technologies Series |
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Place / Publishing House: | Cham : : Springer International Publishing AG,, 2022. Ã2022. |
Year of Publication: | 2022 |
Edition: | 2nd ed. |
Language: | English |
Series: | Cognitive Technologies Series
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Physical Description: | 1 online resource (349 pages) |
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Brazdil, Pavel. Metalearning : Applications to Automated Machine Learning and Data Mining. 2nd ed. Cham : Springer International Publishing AG, 2022. Ã2022. 1 online resource (349 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier Cognitive Technologies Series Intro -- Preface -- Contents -- Part I Basic Concepts and Architecture -- 1 Introduction -- 1.1 Organization of the Book -- 1.2 Basic Concepts and Architecture (Part I) -- 1.3 Advanced Techniques and Methods (Part II) -- 1.4 Repositories of Experimental Results (Part III) -- References -- 2 Metalearning Approaches for Algorithm Selection I (Exploiting Rankings) -- 2.1 Introduction -- 2.2 Different Forms of Recommendation -- 2.3 Ranking Models for Algorithm Selection -- 2.4 Using a Combined Measure of Accuracy and Runtime -- 2.5 Extensions and Other Approaches -- References -- 3 Evaluating Recommendations of Metalearning/AutoML Systems -- 3.1 Introduction -- 3.2 Methodology for Evaluating Base-Level Algorithms -- 3.3 Normalization of Performance for Base-Level Algorithms -- 3.4 Methodology for Evaluating Metalearning and AutoML Systems -- 3.5 Evaluating Recommendations by Correlation -- 3.6 Evaluating the Effects of Recommendations -- 3.7 Some Useful Measures -- References -- 4 Dataset Characteristics (Metafeatures) -- 4.1 Introduction -- 4.2 Data Characterization Used in Classification Tasks -- 4.3 Data Characterization Used in Regression Tasks -- 4.4 Data Characterization Used in Time Series Tasks -- 4.5 Data Characterization Used in Clustering Tasks -- 4.6 Deriving New Features from the Basic Set -- 4.7 Selection of Metafeatures -- 4.8 Algorithm-Specific Characterization and Representation Issues -- 4.9 Establishing Similarity Between Datasets -- References -- 5 Metalearning Approaches for Algorithm Selection II -- 5.1 Introduction -- 5.2 Using Regression Models in Metalearning Systems -- 5.3 Using Classification at Meta-level for the Prediction of Applicability -- 5.4 Methods Based on Pairwise Comparisons -- 5.5 Pairwise Approach for a Set of Algorithms -- 5.6 Iterative Approach of Conducting Pairwise Tests -- 5.7 Using ART Trees and Forests. 5.8 Active Testing -- 5.9 Non-propositional Approaches -- References -- 6 Metalearning for Hyperparameter Optimization -- 6.1 Introduction -- 6.2 Basic Hyperparameter Optimization Methods -- 6.3 Bayesian Optimization -- 6.4 Metalearning for Hyperparameter Optimization -- 6.5 Concluding Remarks -- References -- 7 Automating Workflow/Pipeline Design -- 7.1 Introduction -- 7.2 Constraining the Search in Automatic Workflow Design -- 7.3 Strategies Used in Workflow Design -- 7.4 Exploiting Rankings of Successful Plans (Workflows) -- References -- Part II Advanced Techniques and Methods -- 8 Setting Up Configuration Spaces and Experiments -- 8.1 Introduction -- 8.2 Types of Configuration Spaces -- 8.3 Adequacy of Configuration Spaces for Given Tasks -- 8.4 Hyperparameter Importance and Marginal Contribution -- 8.5 Reducing Configuration Spaces -- 8.6 Configuration Spaces in Symbolic Learning -- 8.7 Which Datasets Are Needed? -- 8.8 Complete versus Incomplete Metadata -- 8.9 Exploiting Strategies from Multi-armed Bandits to Schedule Experiments -- 8.10 Discussion -- References -- 9 Combining Base-Learners into Ensembles -- 9.1 Introduction -- 9.2 Bagging and Boosting -- 9.3 Stacking and Cascade Generalization -- 9.4 Cascading and Delegating -- 9.5 Arbitrating -- 9.6 Meta-decision Trees -- 9.7 Discussion -- References -- 10 Metalearning in Ensemble Methods -- 10.1 Introduction -- 10.2 Basic Characteristics of Ensemble Systems -- 10.3 Selection-Based Approaches for Ensemble Generation -- 10.4 Ensemble Learning (per Dataset) -- 10.5 Dynamic Selection of Models (per Instance) -- 10.6 Generation of Hierarchical Ensembles -- 10.7 Conclusions and Future Research -- References -- 11 Algorithm Recommendation for Data Streams -- 11.1 Introduction -- 11.2 Metafeature-Based Approaches -- 11.3 Data Stream Ensembles -- 11.4 Recurring Meta-level Models. 11.5 Challenges for Future Research -- References -- 12 Transfer of Knowledge Across Tasks -- 12.1 Introduction -- 12.2 Background, Terminology, and Notation -- 12.3 Learning Architectures in Transfer Learning -- 12.4 A Theoretical Framework -- References -- 13 Metalearning for Deep Neural Networks -- 13.1 Introduction -- 13.2 Background and Notation -- 13.3 Metric-Based Metalearning -- 13.4 Model-Based Metalearning -- 13.5 Optimization-Based Metalearning -- 13.6 Discussion and Outlook -- References -- 14 Automating Data Science -- 14.1 Introduction -- 14.2 Defining the Current Problem/Task -- 14.3 Identifying the Task Domain and Knowledge -- 14.4 Obtaining the Data -- 14.5 Automating Data Preprocessing and Transformation -- 14.6 Automating Model and Report Generation -- References -- 15 Automating the Design of Complex Systems -- 15.1 Introduction -- 15.2 Exploiting a Richer Set of Operators -- 15.3 Changing the Granularity by Introducing New Concepts -- 15.4 Reusing New Concepts in Further Learning -- 15.5 Iterative Learning -- 15.6 Learning to Solve Interdependent Tasks -- References -- Part III Organizing and Exploiting Metadata -- 16 Metadata Repositories -- 16.1 Introduction -- 16.2 Organizing the World Machine Learning Information -- 16.3 OpenML -- References -- 17 Learning from Metadata in Repositories -- 17.1 Introduction -- 17.2 Performance Analysis of Algorithms per Dataset -- 17.3 Performance Analysis of Algorithms across Datasets -- 17.4 Effect of Specific Data/Workflow Characteristics on Performance -- 17.5 Summary -- References -- 18 Concluding Remarks -- 18.1 Introduction -- 18.2 Form of Metaknowledge Used in Different Approaches -- 18.3 Future Challenges -- References -- Index. Description based on publisher supplied metadata and other sources. Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries. Electronic books. van Rijn, Jan N. Soares, Carlos. Vanschoren, Joaquin. Print version: Brazdil, Pavel Metalearning Cham : Springer International Publishing AG,c2022 9783030670238 ProQuest (Firm) https://ebookcentral.proquest.com/lib/oeawat/detail.action?docID=6893332 Click to View |
language |
English |
format |
eBook |
author |
Brazdil, Pavel. |
spellingShingle |
Brazdil, Pavel. Metalearning : Applications to Automated Machine Learning and Data Mining. Cognitive Technologies Series Intro -- Preface -- Contents -- Part I Basic Concepts and Architecture -- 1 Introduction -- 1.1 Organization of the Book -- 1.2 Basic Concepts and Architecture (Part I) -- 1.3 Advanced Techniques and Methods (Part II) -- 1.4 Repositories of Experimental Results (Part III) -- References -- 2 Metalearning Approaches for Algorithm Selection I (Exploiting Rankings) -- 2.1 Introduction -- 2.2 Different Forms of Recommendation -- 2.3 Ranking Models for Algorithm Selection -- 2.4 Using a Combined Measure of Accuracy and Runtime -- 2.5 Extensions and Other Approaches -- References -- 3 Evaluating Recommendations of Metalearning/AutoML Systems -- 3.1 Introduction -- 3.2 Methodology for Evaluating Base-Level Algorithms -- 3.3 Normalization of Performance for Base-Level Algorithms -- 3.4 Methodology for Evaluating Metalearning and AutoML Systems -- 3.5 Evaluating Recommendations by Correlation -- 3.6 Evaluating the Effects of Recommendations -- 3.7 Some Useful Measures -- References -- 4 Dataset Characteristics (Metafeatures) -- 4.1 Introduction -- 4.2 Data Characterization Used in Classification Tasks -- 4.3 Data Characterization Used in Regression Tasks -- 4.4 Data Characterization Used in Time Series Tasks -- 4.5 Data Characterization Used in Clustering Tasks -- 4.6 Deriving New Features from the Basic Set -- 4.7 Selection of Metafeatures -- 4.8 Algorithm-Specific Characterization and Representation Issues -- 4.9 Establishing Similarity Between Datasets -- References -- 5 Metalearning Approaches for Algorithm Selection II -- 5.1 Introduction -- 5.2 Using Regression Models in Metalearning Systems -- 5.3 Using Classification at Meta-level for the Prediction of Applicability -- 5.4 Methods Based on Pairwise Comparisons -- 5.5 Pairwise Approach for a Set of Algorithms -- 5.6 Iterative Approach of Conducting Pairwise Tests -- 5.7 Using ART Trees and Forests. 5.8 Active Testing -- 5.9 Non-propositional Approaches -- References -- 6 Metalearning for Hyperparameter Optimization -- 6.1 Introduction -- 6.2 Basic Hyperparameter Optimization Methods -- 6.3 Bayesian Optimization -- 6.4 Metalearning for Hyperparameter Optimization -- 6.5 Concluding Remarks -- References -- 7 Automating Workflow/Pipeline Design -- 7.1 Introduction -- 7.2 Constraining the Search in Automatic Workflow Design -- 7.3 Strategies Used in Workflow Design -- 7.4 Exploiting Rankings of Successful Plans (Workflows) -- References -- Part II Advanced Techniques and Methods -- 8 Setting Up Configuration Spaces and Experiments -- 8.1 Introduction -- 8.2 Types of Configuration Spaces -- 8.3 Adequacy of Configuration Spaces for Given Tasks -- 8.4 Hyperparameter Importance and Marginal Contribution -- 8.5 Reducing Configuration Spaces -- 8.6 Configuration Spaces in Symbolic Learning -- 8.7 Which Datasets Are Needed? -- 8.8 Complete versus Incomplete Metadata -- 8.9 Exploiting Strategies from Multi-armed Bandits to Schedule Experiments -- 8.10 Discussion -- References -- 9 Combining Base-Learners into Ensembles -- 9.1 Introduction -- 9.2 Bagging and Boosting -- 9.3 Stacking and Cascade Generalization -- 9.4 Cascading and Delegating -- 9.5 Arbitrating -- 9.6 Meta-decision Trees -- 9.7 Discussion -- References -- 10 Metalearning in Ensemble Methods -- 10.1 Introduction -- 10.2 Basic Characteristics of Ensemble Systems -- 10.3 Selection-Based Approaches for Ensemble Generation -- 10.4 Ensemble Learning (per Dataset) -- 10.5 Dynamic Selection of Models (per Instance) -- 10.6 Generation of Hierarchical Ensembles -- 10.7 Conclusions and Future Research -- References -- 11 Algorithm Recommendation for Data Streams -- 11.1 Introduction -- 11.2 Metafeature-Based Approaches -- 11.3 Data Stream Ensembles -- 11.4 Recurring Meta-level Models. 11.5 Challenges for Future Research -- References -- 12 Transfer of Knowledge Across Tasks -- 12.1 Introduction -- 12.2 Background, Terminology, and Notation -- 12.3 Learning Architectures in Transfer Learning -- 12.4 A Theoretical Framework -- References -- 13 Metalearning for Deep Neural Networks -- 13.1 Introduction -- 13.2 Background and Notation -- 13.3 Metric-Based Metalearning -- 13.4 Model-Based Metalearning -- 13.5 Optimization-Based Metalearning -- 13.6 Discussion and Outlook -- References -- 14 Automating Data Science -- 14.1 Introduction -- 14.2 Defining the Current Problem/Task -- 14.3 Identifying the Task Domain and Knowledge -- 14.4 Obtaining the Data -- 14.5 Automating Data Preprocessing and Transformation -- 14.6 Automating Model and Report Generation -- References -- 15 Automating the Design of Complex Systems -- 15.1 Introduction -- 15.2 Exploiting a Richer Set of Operators -- 15.3 Changing the Granularity by Introducing New Concepts -- 15.4 Reusing New Concepts in Further Learning -- 15.5 Iterative Learning -- 15.6 Learning to Solve Interdependent Tasks -- References -- Part III Organizing and Exploiting Metadata -- 16 Metadata Repositories -- 16.1 Introduction -- 16.2 Organizing the World Machine Learning Information -- 16.3 OpenML -- References -- 17 Learning from Metadata in Repositories -- 17.1 Introduction -- 17.2 Performance Analysis of Algorithms per Dataset -- 17.3 Performance Analysis of Algorithms across Datasets -- 17.4 Effect of Specific Data/Workflow Characteristics on Performance -- 17.5 Summary -- References -- 18 Concluding Remarks -- 18.1 Introduction -- 18.2 Form of Metaknowledge Used in Different Approaches -- 18.3 Future Challenges -- References -- Index. |
author_facet |
Brazdil, Pavel. van Rijn, Jan N. Soares, Carlos. Vanschoren, Joaquin. |
author_variant |
p b pb |
author2 |
van Rijn, Jan N. Soares, Carlos. Vanschoren, Joaquin. |
author2_variant |
r j n v rjn rjnv c s cs j v jv |
author2_role |
TeilnehmendeR TeilnehmendeR TeilnehmendeR |
author_sort |
Brazdil, Pavel. |
title |
Metalearning : Applications to Automated Machine Learning and Data Mining. |
title_sub |
Applications to Automated Machine Learning and Data Mining. |
title_full |
Metalearning : Applications to Automated Machine Learning and Data Mining. |
title_fullStr |
Metalearning : Applications to Automated Machine Learning and Data Mining. |
title_full_unstemmed |
Metalearning : Applications to Automated Machine Learning and Data Mining. |
title_auth |
Metalearning : Applications to Automated Machine Learning and Data Mining. |
title_new |
Metalearning : |
title_sort |
metalearning : applications to automated machine learning and data mining. |
series |
Cognitive Technologies Series |
series2 |
Cognitive Technologies Series |
publisher |
Springer International Publishing AG, |
publishDate |
2022 |
physical |
1 online resource (349 pages) |
edition |
2nd ed. |
contents |
Intro -- Preface -- Contents -- Part I Basic Concepts and Architecture -- 1 Introduction -- 1.1 Organization of the Book -- 1.2 Basic Concepts and Architecture (Part I) -- 1.3 Advanced Techniques and Methods (Part II) -- 1.4 Repositories of Experimental Results (Part III) -- References -- 2 Metalearning Approaches for Algorithm Selection I (Exploiting Rankings) -- 2.1 Introduction -- 2.2 Different Forms of Recommendation -- 2.3 Ranking Models for Algorithm Selection -- 2.4 Using a Combined Measure of Accuracy and Runtime -- 2.5 Extensions and Other Approaches -- References -- 3 Evaluating Recommendations of Metalearning/AutoML Systems -- 3.1 Introduction -- 3.2 Methodology for Evaluating Base-Level Algorithms -- 3.3 Normalization of Performance for Base-Level Algorithms -- 3.4 Methodology for Evaluating Metalearning and AutoML Systems -- 3.5 Evaluating Recommendations by Correlation -- 3.6 Evaluating the Effects of Recommendations -- 3.7 Some Useful Measures -- References -- 4 Dataset Characteristics (Metafeatures) -- 4.1 Introduction -- 4.2 Data Characterization Used in Classification Tasks -- 4.3 Data Characterization Used in Regression Tasks -- 4.4 Data Characterization Used in Time Series Tasks -- 4.5 Data Characterization Used in Clustering Tasks -- 4.6 Deriving New Features from the Basic Set -- 4.7 Selection of Metafeatures -- 4.8 Algorithm-Specific Characterization and Representation Issues -- 4.9 Establishing Similarity Between Datasets -- References -- 5 Metalearning Approaches for Algorithm Selection II -- 5.1 Introduction -- 5.2 Using Regression Models in Metalearning Systems -- 5.3 Using Classification at Meta-level for the Prediction of Applicability -- 5.4 Methods Based on Pairwise Comparisons -- 5.5 Pairwise Approach for a Set of Algorithms -- 5.6 Iterative Approach of Conducting Pairwise Tests -- 5.7 Using ART Trees and Forests. 5.8 Active Testing -- 5.9 Non-propositional Approaches -- References -- 6 Metalearning for Hyperparameter Optimization -- 6.1 Introduction -- 6.2 Basic Hyperparameter Optimization Methods -- 6.3 Bayesian Optimization -- 6.4 Metalearning for Hyperparameter Optimization -- 6.5 Concluding Remarks -- References -- 7 Automating Workflow/Pipeline Design -- 7.1 Introduction -- 7.2 Constraining the Search in Automatic Workflow Design -- 7.3 Strategies Used in Workflow Design -- 7.4 Exploiting Rankings of Successful Plans (Workflows) -- References -- Part II Advanced Techniques and Methods -- 8 Setting Up Configuration Spaces and Experiments -- 8.1 Introduction -- 8.2 Types of Configuration Spaces -- 8.3 Adequacy of Configuration Spaces for Given Tasks -- 8.4 Hyperparameter Importance and Marginal Contribution -- 8.5 Reducing Configuration Spaces -- 8.6 Configuration Spaces in Symbolic Learning -- 8.7 Which Datasets Are Needed? -- 8.8 Complete versus Incomplete Metadata -- 8.9 Exploiting Strategies from Multi-armed Bandits to Schedule Experiments -- 8.10 Discussion -- References -- 9 Combining Base-Learners into Ensembles -- 9.1 Introduction -- 9.2 Bagging and Boosting -- 9.3 Stacking and Cascade Generalization -- 9.4 Cascading and Delegating -- 9.5 Arbitrating -- 9.6 Meta-decision Trees -- 9.7 Discussion -- References -- 10 Metalearning in Ensemble Methods -- 10.1 Introduction -- 10.2 Basic Characteristics of Ensemble Systems -- 10.3 Selection-Based Approaches for Ensemble Generation -- 10.4 Ensemble Learning (per Dataset) -- 10.5 Dynamic Selection of Models (per Instance) -- 10.6 Generation of Hierarchical Ensembles -- 10.7 Conclusions and Future Research -- References -- 11 Algorithm Recommendation for Data Streams -- 11.1 Introduction -- 11.2 Metafeature-Based Approaches -- 11.3 Data Stream Ensembles -- 11.4 Recurring Meta-level Models. 11.5 Challenges for Future Research -- References -- 12 Transfer of Knowledge Across Tasks -- 12.1 Introduction -- 12.2 Background, Terminology, and Notation -- 12.3 Learning Architectures in Transfer Learning -- 12.4 A Theoretical Framework -- References -- 13 Metalearning for Deep Neural Networks -- 13.1 Introduction -- 13.2 Background and Notation -- 13.3 Metric-Based Metalearning -- 13.4 Model-Based Metalearning -- 13.5 Optimization-Based Metalearning -- 13.6 Discussion and Outlook -- References -- 14 Automating Data Science -- 14.1 Introduction -- 14.2 Defining the Current Problem/Task -- 14.3 Identifying the Task Domain and Knowledge -- 14.4 Obtaining the Data -- 14.5 Automating Data Preprocessing and Transformation -- 14.6 Automating Model and Report Generation -- References -- 15 Automating the Design of Complex Systems -- 15.1 Introduction -- 15.2 Exploiting a Richer Set of Operators -- 15.3 Changing the Granularity by Introducing New Concepts -- 15.4 Reusing New Concepts in Further Learning -- 15.5 Iterative Learning -- 15.6 Learning to Solve Interdependent Tasks -- References -- Part III Organizing and Exploiting Metadata -- 16 Metadata Repositories -- 16.1 Introduction -- 16.2 Organizing the World Machine Learning Information -- 16.3 OpenML -- References -- 17 Learning from Metadata in Repositories -- 17.1 Introduction -- 17.2 Performance Analysis of Algorithms per Dataset -- 17.3 Performance Analysis of Algorithms across Datasets -- 17.4 Effect of Specific Data/Workflow Characteristics on Performance -- 17.5 Summary -- References -- 18 Concluding Remarks -- 18.1 Introduction -- 18.2 Form of Metaknowledge Used in Different Approaches -- 18.3 Future Challenges -- References -- Index. |
isbn |
9783030670245 9783030670238 |
callnumber-first |
Q - Science |
callnumber-subject |
Q - General Science |
callnumber-label |
Q334-342 |
callnumber-sort |
Q 3334 3342 |
genre |
Electronic books. |
genre_facet |
Electronic books. |
url |
https://ebookcentral.proquest.com/lib/oeawat/detail.action?docID=6893332 |
illustrated |
Not Illustrated |
dewey-hundreds |
000 - Computer science, information & general works |
dewey-tens |
000 - Computer science, knowledge & systems |
dewey-ones |
006 - Special computer methods |
dewey-full |
006.31 |
dewey-sort |
16.31 |
dewey-raw |
006.31 |
dewey-search |
006.31 |
oclc_num |
1301265010 |
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