Metalearning : : Applications to Automated Machine Learning and Data Mining.
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Superior document: | Cognitive Technologies Series |
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TeilnehmendeR: | |
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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Online Access: | |
Physical Description: | 1 online resource (349 pages) |
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Table of 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.