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Automatic Design of Decision-Tree Induction Algorithms
Paperback
Series: Springerbriefs in Computer Science
DatabasesGeneral ComputersProbability & Statistics
ISBN13: 9783319142302
Publisher: Springer
Published: Mar 3 2015
Pages: 176
Weight: 0.60
Height: 0.40 Width: 6.14 Depth: 9.21
Language: English
Presents a detailed study of the major design components that constitute a top-down decision-tree induction algorithm, including aspects such as split criteria, stopping criteria, pruning and the approaches for dealing with missing values. Whereas the strategy still employed nowadays is to use a 'generic' decision-tree induction algorithm regardless of the data, the authors argue on the benefits that a bias-fitting strategy could bring to decision-tree induction, in which the ultimate goal is the automatic generation of a decision-tree induction algorithm tailored to the application domain of interest. For such, they discuss how one can effectively discover the most suitable set of components of decision-tree induction algorithms to deal with a wide variety of applications through the paradigm of evolutionary computation, following the emergence of a novel field called hyper-heuristics.
Automatic Design of Decision-Tree Induction Algorithms would be highly useful for machine learning and evolutionary computation students and researchers alike.
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