Front Cover; C4.5: Programs for Machine Learning; Copyright Page; Table of Contents; Preface; Obtaining the C4.5 Code; CHAPTER 1. Introduction; 1.1 Example: Labor negotiation settlements; 1.2 Other kinds of classification models; 1.3 What lies ahead; CHAPTER 2. Constructing Decision Trees; 2.1 Divide and conquer; 2.2 Evaluating tests; 2.3 Possible tests considered; 2.4 Tests on continuous attributes; CHAPTER 3. Unknown Attribute Values; 3.1 Adapting the previous algorithms; 3.2 Play/Don't Play example again; 3.3 Recapitulation; CHAPTER 4. Pruning Decision Trees; 4.1 When to simplify?

4.2 Error-based pruning4.3 Example: Democrats and Republicans; 4.4 Estimating error rates for trees; CHAPTER 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; CHAPTER 6. Windowing; 6.1 Example: Hypothyroid conditions revisited; 6.2 Why retain windowing?; 6.3 Example: The multiplexor; CHAPTER 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.

CHAPTER 8. Interacting with Classification Models8.1 Decision tree models; 8.2 Production rule models; 8.3 Caveat; CHAPTER 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; CHAPTER 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; CHAPTER 11. Desirable Additions; 11.1 Continuous classes; 11.2 Ordered discrete attributes; 11.3 Structured attributes; 11.4 Structured induction.

11.5 Incremental induction11.6 Prospectus; Appendix: Program Listings; Brief descriptions of the contents of the files; Notes on some important data structures; File Makefile; Alphabetic index of routines; References and Bibliography; Author Index; Subject Index.