TY  - BOOK
AU  - Barros,Rodrigo C.
AU  - de Carvalho,André C.P.L.F.
AU  - Freitas,Alex A.
ED  - SpringerLink (Online service)
TI  - Automatic Design of Decision-Tree Induction Algorithms
T2  - SpringerBriefs in Computer Science,
SN  - 9783319142319
AV  - QA76.9.D343 
PY  - 2015///
CY  - Cham
PB  - Springer International Publishing, Imprint: Springer
KW  - Data mining
KW  - Optical pattern recognition
KW  - Data Mining and Knowledge Discovery
KW  - Pattern Recognition
N1  - 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
N2  - 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
UR  - https://doi.org/10.1007/978-3-319-14231-9
ER  -