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Metalearning : Applications to Automated Machine Learning and Data Mining.

Bibliographic Details
Main Author: Brazdil, Pavel.
Other Authors: van Rijn, Jan N., Soares, Carlos., Vanschoren, Joaquin.
Format: eBook
Language:English
Published: Cham : Springer International Publishing AG, 2022.
Edition:2nd ed.
Series:Cognitive Technologies Series
Subjects:
Electronic books.
Online Access:Click to View
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.

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