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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.
Marios Polycarpou, André C.P.L.F. de Carvalho, Jeng-Shyang Pan, Michał Woźniak, Héctor Quintián, Emilio Corchado, André C. P. L. F. de Carvalho, André C. P. L. F. De Carvalho, Micha¿ Wo¿niak
José Gaviria de la Puerta, Iván García Ferreira, Pablo Garcia Bringas, Fanny Klett, Ajith Abraham, André C.P.L.F. de Carvalho, Álvaro Herrero, Bruno Baruque, Héctor Quintián, Emilio Corchado, Pablo Gar Ferreira, Iván García, Bringas
Álvaro Herrero, Bruno Baruque, Fanny Klett, Ajith Abraham, Václav Snášel, André C.P.L.F. de Carvalho, Pablo García Bringas, Ivan Zelinka, Héctor Quintián, Emilio Corchado, Václav Sná¿el, André C. P. L. F. De Carvalho