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C4.5: Programs for Machine Learning (Morgan Kaufmann Series in Machine Learning), by J. Ross Quinlan
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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.
- Sales Rank: #1809598 in Books
- Brand: Brand: Morgan Kaufmann
- Published on: 1992-10-15
- Released on: 1992-10-01
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x .71" w x 7.25" l, 1.20 pounds
- Binding: Paperback
- 302 pages
- Used Book in Good Condition
Review
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 its use , the source code * about 8,800 lines), and implementation notes. The source code and sample datasets are also available on a 3.5-inch floppy diskette for a Sun workstation.
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 and software should be of interest to developers of classification-based intelligent systems and to students in machine learning and expert systems courses. -- Book Description
From the Back Cover
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.
About the Author
J. Ross Quinlan, University of New South Wales
Most helpful customer reviews
16 of 17 people found the following review helpful.
Invaluable for serious users of See5 or C5.0
By Thomas Wikman
Despite its age this classic is invaluable to any serious user of See5 (Windows) or C5.0 (UNIX). C4.5 (See5/C5) is a linear classifier system that is often used for machine learning, or as a data mining tool for discovering patterns in databases. The classifiers can be in the form of either decision trees or rule sets. Just like ID3 it employs a "divide and conquer" strategy and uses entropy (information content) to compute its gain ratio (the split criteria).
C5.0 and See5 are built on C4.5, which is open source and free. However, since C5.0 and See5 are commercial products the code and the internals of the See5/C5 algorithms are not public. This is why this book is still so valuable. The first half of the book explains how C4.5 works, and describes its features, for example, partitioning, pruning, and windowing in detail. The book also discusses how C4.5 should be used, and potential problems with over-fit and non-representative data. The second half of the book gives a complete listing of the source code; 8,800 lines of C-code.
C5.0 is faster and more accurate than C4.5 and has features like cross validation, variable misclassification costs, and boost, which are features that C4.5 does not have. However, since minor misuse of See5 could have cost our company tens of millions of dollars it was important that we knew as much as possible about what we were doing, which is why this book was so valuable.
The reasons we did not use, for example, neural networks were:
(1) We had a lot of nominal data (in addition to numeric data)
(2) We had unknown attributes
(3) Our data sets were typically not very large and still we had a lot of attributes
(4) Unlike neural networks, decision trees and rule sets are human readable, possible to comprehend, and can be modified manually if necessary. Since we had problems with non-representative data but understood these problems as well as our system quite well, it was sometimes advantageous for us to modify the decision trees.
If you are in a similar situation I recommend See5/C5 as well as this book.
2 of 2 people found the following review helpful.
Classical book - a bit pricy
By Asterix
This a very classical macihne learning book. The presentation of the material is very lucid. Dr. Quinlan is a great writer. However I would say that the book is a bit pricy. More than half of the book is C4.5 code. I personally would have liked more of the theory part. Also an updated edition with C5.0 algorithm will be very much welcome from the readers. I am not sure whether Dr. Quinlan has a book on C5.0 or is the enhancements over C4.5 are completely proprietory.
Overall, it is a good book to learn about the C4.5 algorithm.
12 of 13 people found the following review helpful.
The most clear work on Decision Trees available !
By A Customer
If you want to get introduced to Decision Trees algorithms, you must read this book. Ross Quinlan is the father of 'C 4.5' the most widely used tree algorithm. Most other algorithms (except for Chaid, which is older) are enhancements to C4.5 If you are from marketing, this is not a book for you. Why didn't you include a disk instead of so much source code pages, Ross ?
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