- author = "Gisele L. Pappa and Alex A. Freitas",
- title = "Automating the Design of Data Mining Algorithms",
- subtitle = "An Evolutionary Computation Approach",
- publisher = "Springer",
- year = "2010",
- volume = "XIII",
- series = "Natural Computing Series",
- ISSN = "1619-7127",
- keywords = "genetic algorithms, genetic programming",
- isbn13 = "978-3-642-02540-2",
-
DOI = "
doi:10.1007/978-3-642-02541-9",
-
abstract = "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.",
-
notes = "
Reviewed in \cite{Woodward:2011:GPEM}",
- size = "187 pages",
