Automating the design of data mining algorithms [electronic resource] : an evolutionary computation approach / Gisele L. Pappa, Alex A. Freitas.
Traditionally, evolutionary computing techniques have been applied in the area of data mining either to optimize the parameters of data mining algorithms or to discover knowledge or patterns in the data, i.e., to directly solve the target data mining problem. This book proposes a different goal for...
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| Format: | Electronic eBook |
| Language: | English |
| Published: |
Heidelberg ; New York :
Springer,
©2010.
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| Series: | Natural computing series.
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| Subjects: |
MARC
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| 245 | 1 | 0 | |a Automating the design of data mining algorithms |h [electronic resource] : |b an evolutionary computation approach / |c Gisele L. Pappa, Alex A. Freitas. |
| 260 | |a Heidelberg ; |a New York : |b Springer, |c ©2010. | ||
| 300 | |a 1 online resource (xiii, 187 pages) : |b illustrations. | ||
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| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a Introduction -- Data Mining -- Evolutionary Algorithms -- Evolutionary Algorithms for Automating the Parameter Setting and the Partial Design of Data Mining Algorithms -- A New Grammar-based Genetic Programming System for Automating the Design of Full Rule Induction Algorithms -- Computational Results on the Automatic Design of Full Induction Algorithms -- Conclusions. | |
| 520 | |a Traditionally, evolutionary computing techniques have been applied in the area of data mining either to optimize the parameters of data mining algorithms or to discover knowledge or patterns in the data, i.e., to directly solve the target data mining problem. This book proposes a different goal for evolutionary algorithms in data mining: to automate the design of a data mining algorithm, rather than just optimize its parameters. The authors first offer introductory overviews on data mining, focusing on rule induction methods, and on evolutionary algorithms, focusing on genetic programming. They then examine the conventional use of evolutionary algorithms to discover classification rules or related types of knowledge structures in the data, before moving to the more ambitious objective of their research, the design of a new genetic programming system for automating the design of full rule induction algorithms. They analyze computational results from their automatically designed algorithms, which show that the machine-designed rule induction algorithms are competitive when compared with state-of-the-art human-designed algorithms. Finally the authors examine future research directions. This book will be useful for researchers and practitioners in the areas of data mining and evolutionary computation. | ||
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