Metalearning : : Applications to Automated Machine Learning and Data Mining.

• Superior document: Cognitive Technologies Series
• Author: Brazdil, Pavel.
• TeilnehmendeR: van Rijn, Jan N.; Soares, Carlos.; Vanschoren, Joaquin.
• Place / Publishing House: Cham : : Springer International Publishing AG,, 2022.; Ã2022.
• Year of Publication: 2022
• Edition: 2nd ed.
• Language: English
• Series: Cognitive Technologies Series
• Physical Description: 1 online resource (349 pages)

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.