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 traditional analytics approaches. The book is ‘meta’ to analytics, covering general analytics in sufficient detail for readers to engage with, and understand, hybrid or meta- approaches. The book has relevance to machine translation, robotics, biological and social sciences, medical and healthcare informatics, economics, business and finance.

Inn addition, the analytics within can be applied to predictive algorithms for everyone from police departments to sports analysts.

Provides comprehensive and systematic coverage of machine learning-based data analysis tasksEnables rapid progress towards competency in data analysis techniquesGives exhaustive and widely applicable patterns for use by data scientistsCovers hybrid or ‘meta’ approaches, along with general analyticsLays out information and practical guidance on data analysis for practitioners working across all sectors

1. Ground truthing
2. Experiment design
3. Meta-Analytic design patterns
4. Sensitivity analysis and big system engineering
5. Multi-path predictive selection
6. Modeling and model fitting: including Antibody model, stem-differentiated cell model, and chemical, physical and environmental models for greater diversity in form
7. Synonym-antonym and Reinforce-Void patterns and their value in data consensus, data anonymization, and data normalization
8. Meta-analytics as analytics around analytics (functional metrics, entropy, EM). Ingesting statistical approaches for specific domains and generalizing them for data hybrid systems
9. System design optimization (entropy, error variance, coupling minimization F-score)
10. Aleatory techniques/expert system techniques…tie to ground truthing and error testing
11. Applications: machine translation, robotics, biological and social sciences, medical and healthcare informatics, economics, business and finance
12. Discussion and Conclusions, and the Future of Data