Overview
- First book only devoted to associative classification, which is an emerging classification strategy
- The work puts associative classification algorithms into the existing machine learning theory
- The work lists several successful application scenarios for associative classification
Part of the book series: SpringerBriefs in Computer Science (BRIEFSCOMPUTER)
Access this book
Tax calculation will be finalised at checkout
Other ways to access
Table of contents (10 chapters)
-
Front Matter
-
Introduction and Preliminaries
-
Front Matter
-
-
Associative Classification
-
Front Matter
-
-
Extensions to Associative Classification
-
Front Matter
-
-
Conclusions and Future Work
-
Front Matter
-
-
Back Matter
Keywords
About this book
The ultimate goal of machines is to help humans to solve problems.
Such problems range between two extremes: structured problems for which the solution is totally defined (and thus are easily programmed by humans), and random problems for which the solution is completely undefined (and thus cannot be programmed). Problems in the vast middle ground have solutions that cannot be well defined and are, thus, inherently hard to program. Machine Learning is the way to handle this vast middle ground, so that many tedious and difficult hand-coding tasks would be replaced by automatic learning methods. There are several machine learning tasks, and this work is focused on a major one, which is known as classification. Some classification problems are hard to solve, but we show that they can be decomposed into much simpler sub-problems. We also show that independently solving these sub-problems by taking into account their particular demands, often leads to improved classification performance.
Authors and Affiliations
Bibliographic Information
Book Title: Demand-Driven Associative Classification
Authors: Adriano Veloso, Wagner Meira Jr.
Series Title: SpringerBriefs in Computer Science
DOI: https://doi.org/10.1007/978-0-85729-525-5
Publisher: Springer London
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer-Verlag London Ltd., part of Springer Nature 2011
Softcover ISBN: 978-0-85729-524-8Published: 19 May 2011
eBook ISBN: 978-0-85729-525-5Published: 18 May 2011
Series ISSN: 2191-5768
Series E-ISSN: 2191-5776
Edition Number: 1
Number of Pages: XIII, 112
Number of Illustrations: 27 b/w illustrations
Topics: Data Mining and Knowledge Discovery, Probability and Statistics in Computer Science