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中图分类法:
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TP181 版次： |
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著者:
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Barros, R. C., |
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题名:
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Automatic design of decision-tree induction algorithms / / , |
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载体形态:
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xii, 176 pages ; 24 cm. |
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内容提要:
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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. |
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主题词:
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Machine learning Mathematics. |
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主题词:
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Decision trees. |
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主要责任者:
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Carvalho, Andr Carlos Ponce de Leon Ferreira, |
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主要责任者:
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Freitas, Alex A., |
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索书号:
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1 |