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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. 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. 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.
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