03573nam 22006255 450 991029922690332120200702212513.03-319-14231-310.1007/978-3-319-14231-9(CKB)3710000000355364(EBL)1966919(SSID)ssj0001451984(PQKBManifestationID)11836221(PQKBTitleCode)TC0001451984(PQKBWorkID)11478726(PQKB)11205341(DE-He213)978-3-319-14231-9(MiAaPQ)EBC1966919(PPN)184495326(EXLCZ)99371000000035536420150204d2015 u| 0engur|n|---|||||txtccrAutomatic Design of Decision-Tree Induction Algorithms /by Rodrigo C. Barros, André C.P.L.F de Carvalho, Alex A. Freitas1st ed. 2015.Cham :Springer International Publishing :Imprint: Springer,2015.1 online resource (184 p.)SpringerBriefs in Computer Science,2191-5768Description based upon print version of record.3-319-14230-5 Includes bibliographical references.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.SpringerBriefs in Computer Science,2191-5768Data miningPattern recognitionData Mining and Knowledge Discoveryhttps://scigraph.springernature.com/ontologies/product-market-codes/I18030Pattern Recognitionhttps://scigraph.springernature.com/ontologies/product-market-codes/I2203XData mining.Pattern recognition.Data Mining and Knowledge Discovery.Pattern Recognition.004006.312006.4Barros Rodrigo Cauthttp://id.loc.gov/vocabulary/relators/aut1061766de Carvalho André C.P.L.Fauthttp://id.loc.gov/vocabulary/relators/autFreitas Alex Aauthttp://id.loc.gov/vocabulary/relators/autBOOK9910299226903321Automatic Design of Decision-Tree Induction Algorithms2520011UNINA