000725525 000__ 02991cam\a2200469Ii\4500 000725525 001__ 725525 000725525 005__ 20230306140643.0 000725525 006__ m\\\\\o\\d\\\\\\\\ 000725525 007__ cr\cn\nnnunnun 000725525 008__ 150206s2015\\\\sz\\\\\\ob\\\\000\0\eng\d 000725525 020__ $$a9783319142319$$qelectronic book 000725525 020__ $$a3319142313$$qelectronic book 000725525 020__ $$z9783319142302 000725525 0247_ $$a10.1007/978-3-319-14231-9$$2doi 000725525 035__ $$aSP(OCoLC)ocn902846585 000725525 035__ $$aSP(OCoLC)902846585 000725525 040__ $$aN$T$$beng$$erda$$epn$$cN$T$$dN$T$$dGW5XE$$dDKU$$dUPM$$dIDEBK$$dCOO$$dOCLCF$$dE7B$$dEBLCP$$dYDXCP$$dVLB 000725525 049__ $$aISEA 000725525 050_4 $$aQA76.9.A43$$bB37 2015eb 000725525 08204 $$a005.1$$223 000725525 1001_ $$aBarros, Rodrigo C.,$$eauthor. 000725525 24510 $$aAutomatic Design of Decision-Tree Induction Algorithms$$h[electronic resource] /$$cRodrigo C. Barros, André C.P.L.F. de Carvalho, Alex A. Freitas. 000725525 264_1 $$aCham [Switzerland] :$$bSpringer,$$c[2015] 000725525 300__ $$a1 online resource. 000725525 336__ $$atext$$btxt$$2rdacontent 000725525 337__ $$acomputer$$bc$$2rdamedia 000725525 338__ $$aonline resource$$bcr$$2rdacarrier 000725525 4901_ $$aSpringerBriefs in computer science 000725525 504__ $$aIncludes bibliographical references. 000725525 5050_ $$aIntroduction -- Decision-Tree Induction -- Evolutionary Algorithms and Hyper-Heuristics -- HEAD-DT: Automatic Design of Decision-Tree Algorithms -- HEAD-DT: Experimental Analysis -- HEAD-DT: Fitness Function Analysis -- Conclusions. 000725525 506__ $$aAccess limited to authorized users. 000725525 520__ $$aPresents a detailed study of the major design components that constitute a top-down decision-tree induction algorithm, including aspects such as split criteria, stopping criteria, pruning and the approaches for dealing with missing values. Whereas the strategy still employed nowadays is to use a 'generic' decision-tree induction algorithm regardless of the data, the authors argue on the benefits that a bias-fitting strategy could bring to decision-tree induction, in which the ultimate goal is the automatic generation of a decision-tree induction algorithm tailored to the application domain of interest. For such, they discuss how one can effectively discover the most suitable set of components of decision-tree induction algorithms to deal with a wide variety of applications through the paradigm of evolutionary computation, following the emergence of a novel field called hyper-heuristics. "Automatic Design of Decision-Tree Induction Algorithms" would be highly useful for machine learning and evolutionary computation students and researchers alike. 000725525 650_0 $$aComputer algorithms. 000725525 650_0 $$aDecision trees. 000725525 7001_ $$aCarvalho, André C.P.L.F. de,$$eauthor. 000725525 7001_ $$aFreitas, Alex A.,$$eauthor. 000725525 77608 $$iPrint version:$$z9783319142302 000725525 830_0 $$aSpringerBriefs in computer science. 000725525 852__ $$bebk 000725525 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-14231-9$$zOnline Access$$91397441.1 000725525 909CO $$ooai:library.usi.edu:725525$$pGLOBAL_SET 000725525 980__ $$aEBOOK 000725525 980__ $$aBIB 000725525 982__ $$aEbook 000725525 983__ $$aOnline 000725525 994__ $$a92$$bISE