Rule Extraction from Support Vector Machines

  • Joachim Diederich

Part of the Studies in Computational Intelligence book series (SCI, volume 80)

Table of contents

  1. Front Matter
    Pages I-XII
  2. Introduction

    1. David Martens, Johan Huysmans, Rudy Setiono, Jan Vanthienen, Bart Baesens
      Pages 33-63
  3. Algorithms and Techniques

    1. Lisa Torrey, Jude Shavlik, Trevor Walker, Richard Maclin
      Pages 67-82
    2. Glenn Fung, Sathyakama Sandilya, R. Bharat Rao
      Pages 83-107
    3. Haydemar Núñez, Cecilio Angulo, Andreu Català
      Pages 109-134
    4. Shaoning Pang, Nik Kasabov
      Pages 135-162
    5. Marcin Blachnik, Włodzisław Duch
      Pages 163-182
  4. Applications

    1. Jieyue He, Hae-jin Hu, Bernard Chen, Phang C. Tai, Rob Harrison, Yi Pan
      Pages 227-252
  5. Back Matter
    Pages 253-262

About this book

Introduction

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.

Keywords

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

Editors and affiliations

  • Joachim Diederich
    • 1
  1. 1.School of Information Technology and Electrical Engineering School of Medicine, Central Clinical DivisionThe University of QueenslandBrisbaneAustralia

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-540-75390-2
  • Copyright Information Springer-Verlag Berlin Heidelberg 2008
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-540-75389-6
  • Online ISBN 978-3-540-75390-2
  • Series Print ISSN 1860-949X
  • About this book