Meta-Analytics

Consensus Approaches and System Patterns for Data Analysis

Steven Simske

• Publisher: Morgan Kaufmann
• Year: 2019
• Print ISBN: 9780128146231
• eBook ISBN: 9780128146248
• Topic: Computer Science
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

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 tasks- Enables rapid progress towards competency in data analysis techniques- Gives exhaustive and widely applicable patterns for use by data scientists- Covers hybrid or 'meta' approaches, along with general analytics- Lays out information and practical guidance on data analysis for practitioners working across all sectors

Chapter 1

Introduction, overview, and applications

Abstract

In this mammoth chapter, we cover the basic background material in statistics, machine learning, and artificial intelligence needed to understand the ever-broadening field of analytics. This chapter also introduces the software, data mining, and knowledge discovery skills necessary for the data scientist to proceed toward meta-analytics, that is, the next generation of analytics in which systems are hybrid by design and use multiple analytics to deduce valuable information about the data. Two longer sections at the end of the chapter will show how to build a classifier from the ground up that incorporates much of the statistical approaches of the earlier sections.

Keywords

Algorithms; Analytics; Artificial intelligence; Deep learning; Deep unlearning; Classification; Data mining; Machine intelligence; Machine learning; Parallelism; Recognition; Statistics; System architecture; Systems

It is a capital mistake to theorize before one has data

Arthur Conan Doyle (1887)

Numquam ponenda est pluralitas sine necessitate

William of Ockham, Duns Scotus, et al. (c. 1300)

E pluribus unum

US Motto

1.1 Introduction

We live in a world in which more data have been collected in the past 2–3 years than were collected in the entire history of the world before then. Based on the trends of the past few years, we’ll be saying this for a while. Why is this the case? The confluence of nearly limitless storage and processing power has, quite simply, made it far easier to generate and preserve data. The most relevant question is, perhaps, not whether this will continue, but rather how much of the data will be used for anything more than filling up storage space.

The machine intelligence community is, of course, interested in turning these data into information and has had tremendous success to date albeit in somewhat specific and/or constrained situations. Recent advancements in hardware—from raw processing power and nearly limitless storage capacity, to the architectural revolution that graphics processing units (GPUs) bring, to parallel and distributed computation—have allowed software developers and algorithm developers to encode processes that were unthinkable with the hardware of even a decade ago. Deep learning and in particular convolutional neural networks, together with dataflow programming, allow for an...

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• Dedication
• Acknowledgments
• Chapter 1: Introduction, overview, and applications
• Chapter 2: Ground truthing
• Chapter 3: Experimental design
• Chapter 4: Meta-analytic design patterns
• Chapter 5: Sensitivity analysis and big system engineering
• Chapter 6: Multipatch predictive selection
• Chapter 7: Modeling and model fitting
• Chapter 8: Synonym-antonym and reinforce-void patterns
• Chapter 9: Analytics around analytics
• Chapter 10: System design optimization
• Chapter 11: Aleatory and expert system techniques
• Chapter 12: Application I: Topics and challenges in machine translation, robotics, and biological sciences
• Chapter 13: Application II: Medical and health-care informatics, economics, business, and finance
• Chapter 14: Discussion, conclusions, and the future of data
• Index