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 ...
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2015
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oai:scholar.dlu.edu.vn:DLU123456789-585882023-11-11T06:16:56Z Automatic Design of Decision-Tree Induction Algorithms Barros, Rodrigo C Freitas, Alex A Computer algorithms Decision trees Mathematical Statistical Software 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. 2015-09-29T08:07:25Z 2015-09-29T08:07:25Z 2015 Book 978-3-319-14231-9 https://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/58588 en application/pdf Springer |
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Thư viện Trường Đại học Đà Lạt |
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Thư viện số |
language |
English |
topic |
Computer algorithms Decision trees Mathematical Statistical Software |
spellingShingle |
Computer algorithms Decision trees Mathematical Statistical Software Barros, Rodrigo C Freitas, Alex A Automatic Design of Decision-Tree Induction Algorithms |
description |
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. |
format |
Book |
author |
Barros, Rodrigo C Freitas, Alex A |
author_facet |
Barros, Rodrigo C Freitas, Alex A |
author_sort |
Barros, Rodrigo C |
title |
Automatic Design of Decision-Tree Induction Algorithms |
title_short |
Automatic Design of Decision-Tree Induction Algorithms |
title_full |
Automatic Design of Decision-Tree Induction Algorithms |
title_fullStr |
Automatic Design of Decision-Tree Induction Algorithms |
title_full_unstemmed |
Automatic Design of Decision-Tree Induction Algorithms |
title_sort |
automatic design of decision-tree induction algorithms |
publisher |
Springer |
publishDate |
2015 |
url |
https://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/58588 |
_version_ |
1819819802628194304 |