Rule Extraction from Support Vector Machines

1. Front Matter

   Pages I-XII

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2. Introduction

   1. Rule Extraction from Support Vector Machines: An Introduction

      • Joachim Diederich

      Pages 3-31

   2. Rule Extraction from Support Vector Machines: An Overview of Issues and Application in Credit Scoring

      • David Martens, Johan Huysmans, Rudy Setiono, Jan Vanthienen, Bart Baesens

      Pages 33-63

3. Algorithms and Techniques

   1. Rule Extraction for Transfer Learning

      • Lisa Torrey, Jude Shavlik, Trevor Walker, Richard Maclin

      Pages 67-82

   2. Rule Extraction from Linear Support Vector Machines via Mathematical Programming

      • Glenn Fung, Sathyakama Sandilya, R. Bharat Rao

      Pages 83-107

   3. Rule Extraction Based on Support and Prototype Vectors

      • Haydemar Núñez, Cecilio Angulo, Andreu Català

      Pages 109-134

   4. SVMT-Rule: Association Rule Mining Over SVM Classification Trees

      • Shaoning Pang, Nik Kasabov

      Pages 135-162

   5. Prototype Rules from SVM

      • Marcin Blachnik, Włodzisław Duch

      Pages 163-182

4. Applications

   1. Prediction of First-Day Returns of Initial Public Offering in the US Stock Market Using Rule Extraction from Support Vector Machines

      • Rolf Mitsdorffer, Joachim Diederich

      Pages 185-203

   2. Accent in Speech Samples: Support Vector Machines for Classification and Rule Extraction

      • Carol Pedersen, Joachim Diederich

      Pages 205-226

   3. Rule Extraction from SVM for Protein Structure Prediction

      • Jieyue He, Hae-jin Hu, Bernard Chen, Phang C. Tai, Rob Harrison, Yi Pan

      Pages 227-252

5. Back Matter

   Pages 253-262

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Support vector machines (SVMs) are one of the most active research areas in machine learning. SVMs have shown good performance in a number of applications, including text and image classification. However, the learning capability of SVMs comes at a cost – an inherent inability to explain in a comprehensible form, the process by which a learning result was reached. Hence, the situation is similar to neural networks, where the apparent lack of an explanation capability has led to various approaches aiming at extracting symbolic rules from neural networks. For SVMs to gain a wider degree of acceptance in fields such as medical diagnosis and security sensitive areas, it is desirable to offer an explanation capability. User explanation is often a legal requirement, because it is necessary to explain how a decision was reached or why it was made. This book provides an overview of the field and introduces a number of different approaches to extracting rules from support vector machines developed by key researchers. In addition, successful applications are outlined and future research opportunities are discussed. The book is an important reference for researchers and graduate students, and since it provides an introduction to the topic, it will be important in the classroom as well. Because of the significance of both SVMs and user explanation, the book is of relevance to data mining practitioners and data analysts.

• Support Vector Machine
• algorithm
• algorithms
• bioinformatics
• classification
• computational intelligence
• computer-aided design (CAD)
• data mining
• intelligence
• knowledge
• learning
• machine learning
• mathematical programming
• optimization
• programming

• School of Information Technology and Electrical Engineering School of Medicine, Central Clinical Division, The University of Queensland, Brisbane, Australia

  Joachim Diederich

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