Automatic Design of Decision-Tree Induction Algorithms
Nonfiction, Computers, Advanced Computing, Engineering, Computer Vision, Database Management, General Computing
| Author: | Rodrigo C. Barros, Alex A. Freitas, André C.P.L.F de Carvalho | ISBN: | 9783319142319 |
| Publisher: | Springer International Publishing | Publication: | February 4, 2015 |
| Imprint: | Springer | Language: | English |
| Author: | Rodrigo C. Barros, Alex A. Freitas, André C.P.L.F de Carvalho |
| ISBN: | 9783319142319 |
| Publisher: | Springer International Publishing |
| Publication: | February 4, 2015 |
| Imprint: | Springer |
| Language: | English |
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.
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.
