C4.5 - Programs for Machine Learning

C4.5 - Programs for Machine Learning

von: J. Ross Quinlan

Elsevier Reference Monographs, 2014

ISBN: 9780080500584 , 313 Seiten

Format: PDF

Kopierschutz: DRM

Windows PC,Mac OSX Apple iPad, Android Tablet PC's

Preis: 54,95 EUR

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C4.5 - Programs for Machine Learning


 

Front Cover

1

C4.5: Programs for Machine Learning

4

Copyright Page

5

Table of Contents

6

Preface

8

Obtaining the C4.5 Code

10

CHAPTER 1. Introduction

12

1.1 Example: Labor negotiation settlements

14

1.2 Other kinds of classification models

23

1.3 What lies ahead

27

CHAPTER 2. Constructing Decision Trees

28

2.1 Divide and conquer

28

2.2 Evaluating tests

31

2.3 Possible tests considered

35

2.4 Tests on continuous attributes

36

CHAPTER 3. Unknown Attribute Values

38

3.1 Adapting the previous algorithms

39

3.2 Play/Don't Play example again

41

3.3 Recapitulation

43

CHAPTER 4. Pruning Decision Trees

46

4.1 When to simplify?

47

4.2 Error-based pruning

48

4.3 Example: Democrats and Republicans

52

4.4 Estimating error rates for trees

53

CHAPTER 5. From Trees to Rules

56

5.1 Generalizing single rules

58

5.2 Class rulesets

61

5.3 Ranking classes and choosing a default

65

5.4 Summary

66

CHAPTER 6. Windowing

68

6.1 Example: Hypothyroid conditions revisited

69

6.2 Why retain windowing?

69

6.3 Example: The multiplexor

71

CHAPTER 7. Grouping Attribute Values

74

7.1 Finding value groups by merging

75

7.2 Example: Soybean diseases

76

7.3 When to form groups?

77

7.4 Example: The Monk's problems

78

7.5 Uneasy reflections

80

CHAPTER 8. Interacting with Classification Models

82

8.1 Decision tree models

82

8.2 Production rule models

89

8.3 Caveat

91

CHAPTER 9. Guide to Using the System

92

9.1 Files

92

9.2 Running the programs

95

9.3 Conducting experiments

100

9.4 Using options: A credit approval example

102

CHAPTER 10. Limitations

106

10.1 Geometric interpretation

106

10.2 Nonrectangular regions

107

10.3 Poorly delineated regions

109

10.4 Fragmented regions

111

10.5 A more cheerful note

113

CHAPTER 11. Desirable Additions

114

11.1 Continuous classes

114

11.2 Ordered discrete attributes

115

11.3 Structured attributes

115

11.4 Structured induction

116

11.5 Incremental induction

117

11.6 Prospectus

118

Appendix: Program Listings

120

Brief descriptions of the contents of the files

121

Notes on some important data structures

123

File Makefile

126

Alphabetic index of routines

299

References and Bibliography

302

Author Index

308

Subject Index

310