Automatic Design of Decision-Tree Induction Algorithms
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.
| Autor: | Barros, Rodrigo C. Freitas, Alex A. de Carvalho, André C.P.L.F |
|---|---|
| ISBN: | 9783319142302 |
| Sprache: | Englisch |
| Seitenzahl: | 176 |
| Produktart: | Kartoniert / Broschiert |
| Verlag: | Springer International Publishing |
| Veröffentlicht: | 03.03.2015 |
| Schlagworte: | Automatic Design Decision trees Evolutionary Computation Hyper-heuristics Machine Learning |
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