Automatic Design of Decision-Tree Induction Algorithms / SpringerBriefs in Computer Science (PDF)
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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.
- Autoren: Rodrigo C. Barros , André C. P. L. F de Carvalho , Alex A. Freitas
- 2015, 2015, 176 Seiten, Englisch
- Verlag: Springer-Verlag GmbH
- ISBN-10: 3319142313
- ISBN-13: 9783319142319
- Erscheinungsdatum: 04.02.2015
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- Dateiformat: PDF
- Größe: 3.98 MB
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