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C4.5 - 1st Edition - ISBN: 9781558602380, 9780080500584


1st Edition

Programs for Machine Learning

Author: J. Quinlan
Paperback ISBN: 9781558602380
eBook ISBN: 9780080500584
Imprint: Morgan Kaufmann
Published Date: 28th June 2014
Page Count: 302
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Classifier systems play a major role in machine learning and knowledge-based systems, and Ross Quinlan's work on ID3 and C4.5 is widely acknowledged to have made some of the most significant contributions to their development. This book is a complete guide to the C4.5 system as implemented in C for the UNIX environment. It contains a comprehensive guide to the system's use , the source code (about 8,800 lines), and implementation notes.

C4.5 starts with large sets of cases belonging to known classes. The cases, described by any mixture of nominal and numeric properties, are scrutinized for patterns that allow the classes to be reliably discriminated. These patterns are then expressed as models, in the form of decision trees or sets of if-then rules, that can be used to classify new cases, with emphasis on making the models understandable as well as accurate. The system has been applied successfully to tasks involving tens of thousands of cases described by hundreds of properties. The book starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting. Advantages and disadvantages of the C4.5 approach are discussed and illustrated with several case studies.

This book should be of interest to developers of classification-based intelligent systems and to students in machine learning and expert systems courses.

Table of Contents

C4.5: Programs for Machine Learning
by J. Ross Quinlan

    How to Obtain the C4.5 Software
    1 Introduction
      1.1 Example: Labor negotiation settlements
      1.2 Other kinds of classification models
      1.3 What lies ahead

    2 Constructing Decision Trees
      2.1 Divide and Conquer
      2.2 Evaluating tests
      2.3 Possible tests considered
      2.4 Tests on continuous attributes

    3 Unknown Attribute Values
      3.1 Adapting the previous algorithms
      3.2 Play/Don't Play example again
      3.3 Recapitulation

    4 Pruning Decision Trees
      4.1 When to simplify?
      4.2 Error-based pruning
      4.3 Example: Democrats and Republicans
      4.4 Estimating error rates for trees

    5 From Trees to Rules
      5.1 Generalizing single rules
      5.2 Class rulesets
      5.3 Ranking classes and choosing a default
      5.4 Summary

    6 Windowing
      6.1 Example: Hypothyroid conditions revisited
      6.2 Why retain windowing?
      6.3 Example: The multiplexor

    7 Grouping Attribute Values
      7.1 Finding value groups by merging
      7.2 Example: Soybean diseases
      7.3 When to form groups
      7.4 Example: The Monk's problems
      7.5 Uneasy reflections

    8 Interacting with Classification Models
      8.1 Decision tree models
      8.2 Production rule models
      8.3 Caveat

    9 Guide to Using the System
      9.1 Files
      9.2 Running the programs
      9.3 Conducting experiments
      9.4 Using options: A credit approval example

    10 Limitations
      10.1 Geometric interpretation
      10.2 Nonrectangular regions
      10.3 Poorly delineated regions
      10.4 Fragmented regions
      10.5 A more cheerful note

    11 Desirable Additions
      11.1 Continuous classes
      11.2 Ordered discrete attributes
      11.3 Structured attributes
      11.4 Structured induction
      11.5 Incremental induction
      11.6 Prospectus

    Appendix: Program Listings
    Brief descriptions of the contents of files
    Notes on some important data structures
    Source code for the system
    Alphabetic index of routines
    References and Bibliography
    Author Index
    Subject Index


No. of pages:
© Morgan Kaufmann 1993
28th June 2014
Morgan Kaufmann
Paperback ISBN:
eBook ISBN:

About the Author

J. Quinlan

J. Ross Quinlan, University of New South Wales

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