Title Statements
- Full title :Ripple-down rules : the alternative to machine learning / Paul Compton, Byeong Ho Kang.
Summary
"Ripple-Down Rules: The Alternative to Machine Learning starts by reviewing the problems with data quality, and the problems with conventional approaches to incorporating expert human knowledge into AI systems. It suggests that problems with knowledge acquisition arise because of mistaken philosophical assumptions about knowledge. It argues people never really explain how they reach a conclusion, rather they justify their conclusion by differentiating between cases in a context, and RDR is based on this more situated understanding of knowledge. The central features of an RDR approach are explained, and detailed worked examples are presented for different types of RDR, based on freely available software developed for this book. The examples ensure developers have a clear enough idea of the simple yet counter-intuitive RDR algorithms to easily build their own RDR systems. It has been proven in industrial application that it takes only a minute or two per rule to build RDR systems with perhaps thousands of rules. The industrial uses of RDR have ranged from medical diagnosis, through data cleansing to chatbots in cars. RDR can be used standalone or to improve the performance of machine learning or other methods"--
Read More ↓Edition Statements
- Publication Date:2021.
- Edition:First edition
Publication Statements
- Publisher(s):CRC Press/Taylor and Francis Group
- Place of Publication:Boca Raton
- Language:eng
- Publication Date:2021.
The Physical Object
- Description:1 online resource (xv, 179 pages)
Edition Identifiers
- ISBN:9781000363685, 1000363686, 9781000363586, 1000363589, 9781003126157, 1003126154
- DOI:10.1201/9781003126157
- oclc:on1246350573
- LC Classification:QA76.76.E95
- Dewey Decimal:006.3/3
Table of Contents
<Blockquote><p>Preface</p></blockquote><blockquote><p>Acknowledgements</p></blockquote><p>1 Problems with Machine Learning and Knowledge Acquisition</p><p>1.1 Introduction</p><p>1.2 Machine Learning</p><p>1.3 Knowledge Acquisition</p><p>2 Philosophical issues in knowledge acquisition</p><p>3 Ripple-Down Rule Overview</p><p>3.1 Case-driven knowledge acquisition</p><p>3.2 Order of cases processed</p><p>3.3 Linked Production Rules</p><p>3.4 Adding rules</p><p>3.5 Assertions and retractions</p><p>3.6 Formulae in conclusion</p><p>4 Introduction to Excel-RDR</p><p>5 Single Classification Example</p><p>5.1 Repetition in an SCRDR knowledge base</p><p>5.2 SCRDR evaluation and machine learning comparison</p><p>5.3 Summary</p><p>6 Multiple classification example</p><p>6.1 Introduction to Multiple Classification Ripple-Down Rules (MCRDR)</p><p>6.2 Excel-MCRDR example</p><p>6.3 Discussion: MCRDR for single classification</p><p>6.4 Actual Multiple classification with MCRDR</p><p>6.5 Discussion</p><p>6.6 Summary</p><p>7 …
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