LEADER 04048nam 22007095 450 001 9910299207703321 005 20200703134715.0 010 $a9783319218588 010 $a3319218581 024 7 $a10.1007/978-3-319-21858-8 035 $a(CKB)3710000000486733 035 $a(EBL)4085623 035 $a(SSID)ssj0001584250 035 $a(PQKBManifestationID)16265330 035 $a(PQKBTitleCode)TC0001584250 035 $a(PQKBWorkID)14865790 035 $a(PQKB)10895776 035 $a(DE-He213)978-3-319-21858-8 035 $a(MiAaPQ)EBC4085623 035 $a(PPN)190527501 035 $a(EXLCZ)993710000000486733 100 $a20151005d2015 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aFeature Selection for High-Dimensional Data /$fby Verónica Bolón-Canedo, Noelia Sánchez-Maroño, Amparo Alonso-Betanzos 205 $a1st ed. 2015. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2015. 215 $a1 online resource (163 p.) 225 1 $aArtificial Intelligence: Foundations, Theory, and Algorithms,$x2365-3051 300 $aDescription based upon print version of record. 311 08$a9783319218571 311 08$a3319218573 320 $aIncludes bibliographical references at the end of each chapters. 327 $aIntroduction to High-Dimensionality -- Foundations of Feature Selection -- Experimental Framework -- Critical Review of Feature Selection Methods -- Application of Feature Selection to Real Problems -- Emerging Challenges. 330 $aThis book offers a coherent and comprehensive approach to feature subset selection in the scope of classification problems, explaining the foundations, real application problems and the challenges of feature selection for high-dimensional data.   The authors first focus on the analysis and synthesis of feature selection algorithms, presenting a comprehensive review of basic concepts and experimental results of the most well-known algorithms. They then address different real scenarios with high-dimensional data, showing the use of feature selection algorithms in different contexts with different requirements and information: microarray data, intrusion detection, tear film lipid layer classification and cost-based features. The book then delves into the scenario of big dimension, paying attention to important problems under high-dimensional spaces, such as scalability, distributed processing and real-time processing, scenarios that open up new and interesting challenges for researchers.   The book is useful for practitioners, researchers and graduate students in the areas of machine learning and data mining. 410 0$aArtificial Intelligence: Foundations, Theory, and Algorithms,$x2365-3051 606 $aArtificial intelligence 606 $aData mining 606 $aData structures (Computer science) 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 606 $aData Structures$3https://scigraph.springernature.com/ontologies/product-market-codes/I15017 615 0$aArtificial intelligence. 615 0$aData mining. 615 0$aData structures (Computer science) 615 14$aArtificial Intelligence. 615 24$aData Mining and Knowledge Discovery. 615 24$aData Structures. 676 $a006.312 700 $aBolón-Canedo$b Verónica$4aut$4http://id.loc.gov/vocabulary/relators/aut$01058124 702 $aSánchez-Maroño$b Noelia$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aAlonso-Betanzos$b Amparo$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910299207703321 996 $aFeature Selection for High-Dimensional Data$92521611 997 $aUNINA