Automating the Design of Data Mining Algorithms

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106,99 

An Evolutionary Computation Approach, Natural Computing Series

ISBN: 3642025404
ISBN 13: 9783642025402
Autor: Pappa, Gisele L/Freitas, Alex
Verlag: Springer Verlag GmbH
Umfang: xiii, 187 S., 33 s/w Illustr., 187 p. 33 illus.
Erscheinungsdatum: 10.11.2009
Auflage: 1/2009
Produktform: Gebunden/Hardback
Einband: GEB

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.Includes supplementary material: sn.pub/extras

Artikelnummer: 1890017 Kategorie:

Beschreibung

Data mining is a very active research area with many successful real-world app- cations. It consists of a set of concepts and methods used to extract interesting or useful knowledge (or patterns) from real-world datasets, providing valuable support for decision making in industry, business, government, and science. Although there are already many types of data mining algorithms available in the literature, it is still dif cult for users to choose the best possible data mining algorithm for their particular data mining problem. In addition, data mining al- rithms have been manually designed; therefore they incorporate human biases and preferences. This book proposes a new approach to the design of data mining algorithms. - stead of relying on the slow and ad hoc process of manual algorithm design, this book proposes systematically automating the design of data mining algorithms with an evolutionary computation approach. More precisely, we propose a genetic p- gramming system (a type of evolutionary computation method that evolves c- puter programs) to automate the design of rule induction algorithms, a type of cl- si cation method that discovers a set of classi cation rules from data. We focus on genetic programming in this book because it is the paradigmatic type of machine learning method for automating the generation of programs and because it has the advantage of performing a global search in the space of candidate solutions (data mining algorithms in our case), but in principle other types of search methods for this task could be investigated in the future.

Inhaltsverzeichnis

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.

Autorenporträt

InhaltsangabeData Mining.- Evolutionary Algorithms.- Genetic Programming for Classification and Algorithm Design.- Automating the Design of Rule Induction Algorithms.- Computational Results on the Automatic Design of Full Rule Induction Algorithms.- Directions for Future Research on the Automatic Design of Data Mining Algorithms.

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