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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020 | |z 9783030670238 | ||
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050 | 4 | |a Q334-342 | |
082 | 0 | |a 006.31 | |
100 | 1 | |a Brazdil, Pavel. | |
245 | 1 | 0 | |a Metalearning : |b Applications to Automated Machine Learning and Data Mining. |
250 | |a 2nd ed. | ||
264 | 1 | |a Cham : |b Springer International Publishing AG, |c 2022. | |
264 | 4 | |c Ã2022. | |
300 | |a 1 online resource (349 pages) | ||
336 | |a text |b txt |2 rdacontent | ||
337 | |a computer |b c |2 rdamedia | ||
338 | |a online resource |b cr |2 rdacarrier | ||
490 | 1 | |a Cognitive Technologies Series | |
505 | 0 | |a 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. | |
505 | 8 | |a 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. | |
505 | 8 | |a 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. | |
588 | |a Description based on publisher supplied metadata and other sources. | ||
590 | |a Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries. | ||
655 | 4 | |a Electronic books. | |
700 | 1 | |a van Rijn, Jan N. | |
700 | 1 | |a Soares, Carlos. | |
700 | 1 | |a Vanschoren, Joaquin. | |
776 | 0 | 8 | |i Print version: |a Brazdil, Pavel |t Metalearning |d Cham : Springer International Publishing AG,c2022 |z 9783030670238 |
797 | 2 | |a ProQuest (Firm) | |
830 | 0 | |a Cognitive Technologies Series | |
856 | 4 | 0 | |u https://ebookcentral.proquest.com/lib/oeawat/detail.action?docID=6893332 |z Click to View |