LEADER 02916oam 2200721I 450 001 9910799907903321 005 20200520144314.0 010 $a0-429-10650-5 010 $a1-4398-3928-X 024 7 $a10.1201/b11423 035 $a(CKB)2670000000175763 035 $a(EBL)830224 035 $a(SSID)ssj0001139458 035 $a(PQKBManifestationID)11651054 035 $a(PQKBTitleCode)TC0001139458 035 $a(PQKBWorkID)11213598 035 $a(PQKB)10959379 035 $a(SSID)ssj0000580990 035 $a(PQKBManifestationID)12234789 035 $a(PQKBTitleCode)TC0000580990 035 $a(PQKBWorkID)10526328 035 $a(PQKB)11497283 035 $a(Au-PeEL)EBL830224 035 $a(CaPaEBR)ebr10546312 035 $a(CaONFJC)MIL692885 035 $a(OCoLC)899154945 035 $a(OCoLC)785416992 035 $a(CaSebORM)9781439839287 035 $a(MiAaPQ)EBC830224 035 $a(EXLCZ)992670000000175763 100 $a20180331d2012 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aCost-sensitive machine learning /$fedited by Balaji Krishnapuram, Shipeng Yu, Bharat Rao 205 $a1st edition 210 1$aBoca Raton, Fla. :$cCRC Press,$d2012. 215 $a1 online resource (316 p.) 225 1 $aChapman & Hall/CRC machine learning & pattern recognition series 300 $a"A Chapman & Hall book." 311 $a1-4665-4817-7 311 $a1-322-61603-5 311 $a1-4398-3925-5 320 $aIncludes bibliographical references. 327 $apt. 1. Theoretical underpinnings of cost-sensitive machine learning -- pt. 2. Cost-sensitive machine learning applications. 330 $aIn machine learning applications, practitioners must take into account the cost associated with the algorithm. These costs include: Cost of acquiring training dataCost of data annotation/labeling and cleaningComputational cost for model fitting, validation, and testingCost of collecting features/attributes for test dataCost of user feedback collectionCost of incorrect prediction/classificationCost-Sensitive Machine Learning is one of the first books to provide an overview of the current research efforts and problems in this area. It discusses real-world applications that incorporate the cost o 410 0$aChapman & Hall/CRC machine learning & pattern recognition series. 517 3 $aCost sensitive machine learning 606 $aMachine learning$xCost effectiveness 615 0$aMachine learning$xCost effectiveness. 676 $a006.31 701 $aKrishnapuram$b Balaji$01587700 701 $aYu$b Shipeng$0920799 701 $aRao$b Bharat$0868293 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910799907903321 996 $aCost-sensitive machine learning$93875960 997 $aUNINA