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.
| Autorius: | Rodrigo C. Barros, Alex A. Freitas, André C. P. L. F de Carvalho, |
| Serija: | SpringerBriefs in Computer Science |
| Leidėjas: | Springer Nature Switzerland |
| Išleidimo metai: | 2015 |
| Knygos puslapių skaičius: | 188 |
| ISBN-10: | 3319142305 |
| ISBN-13: | 9783319142302 |
| Formatas: | Knyga minkštu viršeliu |
| Kalba: | Anglų |
| Žanras: | Expert systems / knowledge-based systems |
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