Machine Learning, Neural and Statistical Classification
Authors :
Donald Michie,
The University of Edinburgh,
David J. Spiegelhalter,
MRC Biostatistics Unit, Cambridge and
Charles C. Taylor,
University of Leeds
Publisher :
Ellis Horwood
Publication Date : February 1994
Terms and Conditions:
| C.C. Taylor wrote: |
| The above book (originally published in 1994 by Ellis Horwood) is now out of print. The copyright now resides with the editors who have decided to make the material freely available on the web. |
The aim of this book is to provide an up-to-date review of different approaches to
classification, compare their performance on a wide range of challenging data-sets, and draw conclusions on their applicability to realistic industrial problems.
As the book's title suggests. a wide variety of approaches has been taken towards this task. Three main historical strands of research can be identified:
statistical,
machine learning and
neural network. These have largely involved different professional and academic groups, and emphasised different issues. All groups have, however. had some objectives in common. They have all attempted to derive procedures that would be able:
- to equal, if not exceed, a human decision-maker's behaviour. but have the advantage of consistency and, to a variable extent. explicitness,
- to handle a wide variety of problems and, given enough data, to be extremely general,
- to be used in practical settings with proven success.
The present text has been produced by a variety of authors, from widely differing backgrounds, but with the common aim of making the results of the
StatLog project accessible to a wide range of workers in the fields of machine learning, statistics and neural networks. and to help the cross-fertilisation of ideas between these groups.
After discussing the general classification problem in Chapter 2, the next 4 chapters detail the methods that have been investigated, divided up according to broad headings of Classical statistics, modern statistical techniques, Decision Trees and Rules, and Neural Networks. The next part of the book concerns the evaluation experiments, and includes chapters on evaluation criteria, a survey of previous comparative studies, a description of the data-sets and the results for the different methods, and an analysis of the results which explores the characteristics of data-sets that make them suitable for particular approaches: we might call this "machine learning on machine learning". The conclusions concerning the experiments are summarised in Chapter 11.
The final chapters of the book broaden the interpretation of the basic classification problem. The fundamental theme of representing knowledge using different formalisms is discussed with relation to constructing classification techniques, followed by a summary of current approaches to dynamic control now arising from a rephrasing of the problem in terms of classification and learning.
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