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[null,"1.","Record Nr.",null,"UNINA9910299226903321"]
[null,null,"Autore",null,"Barros Rodrigo C."]
[null,null,"Titolo",null,"Automatic Design of Decision-Tree Induction Algorithms / / by Rodrigo C. Barros, André C.P.L.F de Carvalho, Alex A. Freitas"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015"]
[null,null,"ISBN",null,"3-319-14231-3"]
[null,null,"Edizione",null,"[1st ed. 2015.]"]
[null,null,"Descrizione fisica",null,"1 online resource (184 p.)"]
[null,null,"Collana",null,"SpringerBriefs in Computer Science, , 2191-5768"]
[null,null,"Disciplina",null,"004"]
[{"$nulls":4},"006.312"]
[{"$nulls":4},"006.4"]
[null,null,"Soggetti",null,"Data mining"]
[{"$nulls":4},"Pattern perception"]
[{"$nulls":4},"Data Mining and Knowledge Discovery"]
[{"$nulls":4},"Pattern Recognition"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references."]
[null,null,"Nota di contenuto",null,"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."]
[null,null,null,"Sommario/riassunto","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."]