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.

List of contents

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.

Summary

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.

Product details

• Authors: Rodrigo Barros, Rodrigo C Barros, Rodrigo C. Barros, André C. P. L. F. de Carvalho, André Ponce de Leon F. de Carvalho, André C P L de Carvalho, André C P L F de Carvalho, André C. P. L. F de Carvalho, André C. P. L. F. De Carvalho, André C.P.L.F de Carvalho, André C.P.L.F. de Carvalho, Freit, Alex Freitas, Alex A. Freitas
• Publisher: Springer, Berlin
• Languages: English
• Product format: Paperback / Softback
• Released: 01.01.2015
• EAN: 9783319142302
• ISBN: 978-3-31-914230-2
• No. of pages: 176
• Dimensions: 154 mm x 9 mm x 239 mm
• Weight: 299 g
• Illustrations: XII, 176 p. 18 illus.
• Series: SpringerBriefs in Computer Science; SpringerBriefs in Computer Science
• Subjects: Natural sciences, medicine, IT, technology > IT, data processing > IT; C, Data Mining, Mustererkennung, Wissensbasierte Systeme, Expertensysteme, computer science, pattern recognition, Data Mining and Knowledge Discovery, Automated Pattern Recognition, Expert systems / knowledge-based systems