LEADER 03573nam 22006255 450 001 9910299226903321 005 20200702212513.0 010 $a3-319-14231-3 024 7 $a10.1007/978-3-319-14231-9 035 $a(CKB)3710000000355364 035 $a(EBL)1966919 035 $a(SSID)ssj0001451984 035 $a(PQKBManifestationID)11836221 035 $a(PQKBTitleCode)TC0001451984 035 $a(PQKBWorkID)11478726 035 $a(PQKB)11205341 035 $a(DE-He213)978-3-319-14231-9 035 $a(MiAaPQ)EBC1966919 035 $a(PPN)184495326 035 $a(EXLCZ)993710000000355364 100 $a20150204d2015 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aAutomatic Design of Decision-Tree Induction Algorithms /$fby Rodrigo C. Barros, André C.P.L.F de Carvalho, Alex A. Freitas 205 $a1st ed. 2015. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2015. 215 $a1 online resource (184 p.) 225 1 $aSpringerBriefs in Computer Science,$x2191-5768 300 $aDescription based upon print version of record. 311 $a3-319-14230-5 320 $aIncludes bibliographical references. 327 $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. 330 $aPresents a detailed study of the major design components that constitute a top-down decision-tree induction algorithm, including aspects such as split criteria, stopping criteria, pruning and the approaches for dealing with missing values. Whereas the strategy still employed nowadays is to use a 'generic' decision-tree induction algorithm regardless of the data, the authors argue on the benefits that a bias-fitting strategy could bring to decision-tree induction, in which the ultimate goal is the automatic generation of a decision-tree induction algorithm tailored to the application domain of interest. For such, they discuss how one can effectively discover the most suitable set of components of decision-tree induction algorithms to deal with a wide variety of applications through the paradigm of evolutionary computation, following the emergence of a novel field called hyper-heuristics. "Automatic Design of Decision-Tree Induction Algorithms" would be highly useful for machine learning and evolutionary computation students and researchers alike. 410 0$aSpringerBriefs in Computer Science,$x2191-5768 606 $aData mining 606 $aPattern recognition 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 615 0$aData mining. 615 0$aPattern recognition. 615 14$aData Mining and Knowledge Discovery. 615 24$aPattern Recognition. 676 $a004 676 $a006.312 676 $a006.4 700 $aBarros$b Rodrigo C$4aut$4http://id.loc.gov/vocabulary/relators/aut$01061766 702 $ade Carvalho$b André C.P.L.F$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aFreitas$b Alex A$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910299226903321 996 $aAutomatic Design of Decision-Tree Induction Algorithms$92520011 997 $aUNINA