Meta-analytics : consensus approaches and system patterns for data analysis

Meta-analytics consensus approaches and system patterns for data analysis

Meta-Analytics: Consensus Approaches and System Patterns for Data Analysis presents an exhaustive set of patterns for data science to use on any machine learning based data analysis task. The book virtually ensures that at least one pattern will lead to better overall system behavior than the use of...

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Detalles Bibliográficos
Otros Autores: Simske, Steven, author (author)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Cambridge, Massachusetts : Morgan Kaufmann Publishers [2019]
Edición:First edition
Materias:
Data mining.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630642406719
Tabla de Contenidos:
  • Front Cover
  • Meta-Analytics: Consensus Approaches and System Patterns for Data Analysis
  • Copyright
  • Dedication
  • Contents
  • Acknowledgments
  • Chapter 1: Introduction, overview, and applications
  • 1.1. Introduction
  • 1.2. Why is this book important?
  • 1.3. Organization of the book
  • 1.4. Informatics
  • 1.5. Statistics for analytics
  • 1.5.1. Value and variance
  • 1.5.2. Sample and population tests
  • 1.5.3. Regression and estimation
  • 1.6. Algorithms for analytics
  • 1.6.1. k-Means and k-nearest neighbor clustering
  • 1.6.2. Unclustering
  • 1.6.3. Markov models
  • 1.7. Machine learning
  • 1.7.1. Entropy
  • 1.7.2. SVM and kernels
  • 1.7.3. Probability
  • 1.7.4. Dimensionality reduction and information gain
  • 1.7.5. Optimization and search
  • 1.7.6. Data mining and knowledge discovery
  • 1.7.7. Recognition
  • 1.7.8. Ensemble learning
  • 1.8. Artificial intelligence
  • 1.8.1. Genetic algorithms
  • 1.8.2. Neural networks
  • 1.8.3. Immunological algorithms
  • 1.9. A platform for building a classifier from the ground up (binary case)
  • 1.10. A platform for building a classifier from the ground up (general case)
  • 1.10.1. Training and validation
  • 1.10.2. Testing and deployment
  • 1.10.3. Comparing training and testing data set results
  • 1.11. Summary
  • References
  • Further reading
  • Chapter 2: Ground truthing
  • 2.1. Introduction
  • 2.2. Pre-validation
  • 2.3. Optimizing settings from training data
  • 2.4. Learning how to Learn
  • 2.5. Deep learning to deep unlearning
  • 2.6. Summary
  • References
  • Chapter 3: Experimental design
  • 3.1. Introduction
  • 3.2. Data normalization
  • 3.2.1. Simple (unambiguous) normalization
  • 3.2.2. Bias normalization
  • 3.2.3. Normalization and experimental design tables
  • 3.3. Designs for the pruning of aging data
  • 3.4. Systems of systems
  • 3.4.1. Systems
  • 3.4.2. Hybrid systems.
  • 3.4.3. Dynamically-updated systems
  • 3.4.4. Interfaces
  • 3.4.5. Gain
  • 3.4.6. Domain normalization
  • 3.4.7. Sensitivity analysis
  • 3.5. Summary
  • References
  • Chapter 4: Meta-analytic design patterns
  • 4.1. Introduction
  • 4.2. Cumulative response patterns
  • 4.2.1. Identifying zones of interest
  • 4.2.2. Zones of interest for sequence-dependent predictive selection
  • 4.2.3. Traditional cumulative gain curves, or lift curves
  • 4.3. Optimization of analytics
  • 4.3.1. Decision trees
  • 4.3.2. Putative-identity triggered patterns
  • 4.3.3. Expectation-maximization and maximum-minimum patterns
  • 4.4. Model agreement patterns
  • 4.4.1. Hybrid regression
  • 4.4.2. Modeling and model fitting
  • 4.5. Co-occurrence and similarity patterns
  • 4.6. Sensitivity analysis patterns
  • 4.7. Confusion matrix patterns
  • 4.8. Entropy patterns
  • 4.9. Independence pattern
  • 4.10. Functional NLP patterns (macro-feedback)
  • 4.11. Summary
  • References
  • Chapter 5: Sensitivity analysis and big system engineering
  • 5.1. Introduction
  • 5.2. Sensitivity analysis of the data set itself
  • 5.3. Sensitivity analysis of the solution model
  • 5.4. Sensitivity analysis of the individual algorithms
  • 5.5. Sensitivity analysis of the hybrid algorithmics
  • 5.6. Sensitivity analysis of the path to the current state
  • 5.7. Summary
  • References
  • Chapter 6: Multipatch predictive selection
  • 6.1. Introduction
  • 6.2. Predictive selection
  • 6.3. Means of predicting
  • 6.4. Means of selecting
  • 6.5. Multi-path approach
  • 6.6. Applications
  • 6.7. Sensitivity analysis
  • 6.8. Summary
  • Reference
  • Chapter 7: Modeling and model fitting
  • 7.1. Introduction
  • 7.2. Chemistry analogues for analytics
  • 7.3. Organic chemistry analogues for analytics
  • 7.4. Immunological and biological analogues for analytics.
  • 7.5. Anonymization analogues for model design and fitting
  • 7.6. LSE, error variance, and entropy: Goodness of fit
  • 7.7. Make mine multiple models!
  • 7.8. Summary
  • References
  • Chapter 8: Synonym-antonym and reinforce-void patterns
  • 8.1. Introduction
  • 8.2. Synonym-antonym patterns
  • 8.3. Reinforce-void patterns
  • 8.4. Broader applicability of these patterns
  • 8.5. Summary
  • References
  • Further reading
  • Chapter 9: Analytics around analytics
  • 9.1. Introduction
  • 9.2. Analytics around analytics
  • 9.2.1. Entropy and occurrence vectors
  • 9.2.2. Functional metrics
  • 9.2.3. E-M (expectation-maximization) approaches
  • 9.2.4. System design concerns
  • 9.3. Optimizing settings from training data
  • 9.4. Hybrid methods
  • 9.5. Other areas for investigation around the analytics
  • 9.6. Summary
  • References
  • Further reading
  • Chapter 10: System design optimization
  • 10.1. Introduction
  • 10.1.1. System considerations-Revisiting the system gains
  • 10.1.2. System gains-Revisiting and expanding the system biases
  • 10.1.3. Nothing ventured, nothing gained
  • 10.2. Module optimization
  • 10.3. Clustering and regularization
  • 10.3.1. Sum of squares regularization
  • 10.3.2. Variance regularization
  • 10.3.3. Cluster size regularization
  • 10.3.4. Small cluster regularization
  • 10.3.5. Number of clusters regularization
  • 10.3.6. Discussion of regularization methods
  • 10.4. Analytic system optimization
  • 10.5. Summary
  • References
  • Chapter 11: Aleatory and expert system techniques
  • 11.1. Introduction
  • 11.2. Revisiting two earlier aleatory patterns
  • 11.2.1. Sequential removal of features aleatory pattern
  • 11.2.2. Sequential variation of feature output aleatory pattern
  • 11.3. Adding random elements for testing
  • 11.4. Hyperspectral aleatory approaches.
  • 11.5. Other aleatory applications in machine and statistical learning
  • 11.6. Expert system techniques
  • 11.7. Summary
  • References
  • Further reading
  • Chapter 12: Application I: Topics and challenges in machine translation, robotics, and biological sciences
  • 12.1. Introduction
  • 12.2. Machine translation
  • 12.3. Robotics
  • 12.4. Biological sciences
  • 12.5. Summary
  • References
  • Chapter 13: Application II: Medical and health-care informatics, economics, business, and finance
  • 13.1. Introduction
  • 13.2. Healthcare
  • 13.3. Economics
  • 13.4. Business and finance
  • 13.5. Summary
  • 13.6. Postscript: Psychology
  • References
  • Chapter 14: Discussion, conclusions, and the future of data
  • 14.1. Chapter 1
  • 14.2. Chapter 2
  • 14.3. Chapter 3
  • 14.4. Chapter 4
  • 14.5. Chapter 5
  • 14.6. Chapter 6
  • 14.7. Chapter 7
  • 14.8. Chapter 8
  • 14.9. Chapter 9
  • 14.10. Chapter 10
  • 14.11. Chapter 11
  • 14.12. Chapter 12
  • 14.13. Chapter 13
  • 14.14. The future of meta-analytics
  • Index
  • Back Cover.

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