Rodrigo C. Barros & André C.P.L.F de Carvalho 
Automatic Design of Decision-Tree Induction Algorithms [PDF ebook] 

Apoio

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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Tabela de Conteúdo

Introduction.- Decision-Tree Induction.- Evolutionary Algorithms and Hyper-Heuristics.- HEAD-DT: Automatic Design of Decision-Tree Algorithms.- HEAD-DT: Experimental Analysis.- HEAD-DT: Fitness Function Analysis.- Conclusions.

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Língua Inglês ● Formato PDF ● Páginas 176 ● ISBN 9783319142319 ● Tamanho do arquivo 4.2 MB ● Editora Springer International Publishing ● Cidade Cham ● País CH ● Publicado 2015 ● Carregável 24 meses ● Moeda EUR ● ID 5234084 ● Proteção contra cópia DRM social

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