C4.5: Programs for Machine Learning

Front Cover
Elsevier, Jun 28, 2014 - Computers - 312 pages

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.



CHAPTER 1 Introduction
CHAPTER 2 Constructing Decision Trees
CHAPTER 3 Unknown Attribute Values
CHAPTER 4 Pruning Decision Trees
CHAPTER 5 From Trees to Rules
CHAPTER 6 Windowing
CHAPTER 7 Grouping Attribute Values
CHAPTER 8 Interacting with Classification Models
CHAPTER 9 Guide to Using the System
CHAPTER 10 Limitations
CHAPTER 11 Desirable Additions
Program Listings
Author Index
Subject Index

Common terms and phrases

About the author (2014)

J. Ross Quinlan, University of New South Wales

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