Barros, Rodrigo C.
Automatic Design of Decision-Tree Induction Algorithms [electronic resource] / by Rodrigo C. Barros, Andr�e C.P.L.F de Carvalho, Alex A. Freitas. - XII, 176 p. 18 illus. online resource. - SpringerBriefs in Computer Science, 2191-5768 . - SpringerBriefs in Computer Science, .
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.
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.
9783319142319
10.1007/978-3-319-14231-9 doi
Computer science.
Data mining.
Pattern recognition.
Computer Science.
Data Mining and Knowledge Discovery.
Pattern Recognition.
QA76.9.D343
006.312
Automatic Design of Decision-Tree Induction Algorithms [electronic resource] / by Rodrigo C. Barros, Andr�e C.P.L.F de Carvalho, Alex A. Freitas. - XII, 176 p. 18 illus. online resource. - SpringerBriefs in Computer Science, 2191-5768 . - SpringerBriefs in Computer Science, .
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.
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.
9783319142319
10.1007/978-3-319-14231-9 doi
Computer science.
Data mining.
Pattern recognition.
Computer Science.
Data Mining and Knowledge Discovery.
Pattern Recognition.
QA76.9.D343
006.312