LEADER 03466nam 22005775 450 001 9910299935603321 005 20251202115057.0 010 $a3-319-90080-3 024 7 $a10.1007/978-3-319-90080-3 035 $a(CKB)4100000003359663 035 $a(MiAaPQ)EBC5378104 035 $a(DE-He213)978-3-319-90080-3 035 $a(PPN)226697223 035 $a(EXLCZ)994100000003359663 100 $a20180430d2018 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aRecent Advances in Ensembles for Feature Selection /$fby Verónica Bolón-Canedo, Amparo Alonso-Betanzos 205 $a1st ed. 2018. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018. 215 $a1 online resource (212 pages) 225 1 $aIntelligent Systems Reference Library,$x1868-4408 ;$v147 311 08$a3-319-90079-X 327 $aBasic concepts -- Feature selection -- Foundations of ensemble learning -- Ensembles for feature selection -- Combination of outputs -- Evaluation of ensembles for feature selection -- Other ensemble approaches -- Applications of ensembles versus traditional approaches: experimental results -- Software tools -- Emerging Challenges. . 330 $aThis book offers a comprehensive overview of ensemble learning in the field of feature selection (FS), which consists of combining the output of multiple methods to obtain better results than any single method. It reviews various techniques for combining partial results, measuring diversity and evaluating ensemble performance. With the advent of Big Data, feature selection (FS) has become more necessary than ever to achieve dimensionality reduction. With so many methods available, it is difficult to choose the most appropriate one for a given setting, thus making the ensemble paradigm an interesting alternative. The authors first focus on the foundations of ensemble learning and classical approaches, before diving into the specific aspects of ensembles for FS, such as combining partial results, measuring diversity and evaluating ensemble performance. Lastly, the book shows examples of successful applications of ensembles for FS and introduces the new challenges thatresearchers now face. As such, the book offers a valuable guide for all practitioners, researchers and graduate students in the areas of machine learning and data mining. . 410 0$aIntelligent Systems Reference Library,$x1868-4408 ;$v147 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aPattern recognition systems 606 $aComputational Intelligence 606 $aArtificial Intelligence 606 $aAutomated Pattern Recognition 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 0$aPattern recognition systems. 615 14$aComputational Intelligence. 615 24$aArtificial Intelligence. 615 24$aAutomated Pattern Recognition. 676 $a006.3 700 $aBolón-Canedo$b Verónica$4aut$4http://id.loc.gov/vocabulary/relators/aut$01058124 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 $a9910299935603321 996 $aRecent Advances in Ensembles for Feature Selection$92497370 997 $aUNINA