1993 C45

• (Quinlan, 1993a) ⇒ J. Ross Quinlan. (1993). “C4.5: Programs for machine learning.” Morgan Kaufmann. ISBN:1558602380

Subject Headings: C4.5 Algorithm, C4.5 System

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Book Overview

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. The source code and sample datasets are also available for download (see below).

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.

Table of Contents

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

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
1993 C45	J. Ross Quinlan			C4.5: Programs for machine learning						1993