Automatic Design of Decision-Tree Induction Algorithms 2015 Edition Contributor(s): Barros, Rodrigo C. (Author), de Carvalho, André C. P. L. F. (Author), Freitas, Alex a. (Author) |
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ISBN: 3319142305 ISBN-13: 9783319142302 Publisher: Springer OUR PRICE: $56.99 Product Type: Paperback - Other Formats Published: March 2015 |
Additional Information |
BISAC Categories: - Computers | Databases - Data Mining - Computers | Computer Vision & Pattern Recognition - Mathematics | Probability & Statistics - General |
Dewey: 006.312 |
Series: Springerbriefs in Computer Science |
Physical Information: 0.4" H x 6.14" W x 9.21" (0.60 lbs) 176 pages |
Descriptions, Reviews, Etc. |
Publisher 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. "Automatic Design of Decision-Tree Induction Algorithms" would be highly useful for machine learning and evolutionary computation students and researchers alike. |