LEADER 03220nam 22005295 450 001 9910483789203321 005 20200701155539.0 010 $a3-030-04663-X 024 7 $a10.1007/978-3-030-04663-7 035 $a(CKB)4100000007158850 035 $a(DE-He213)978-3-030-04663-7 035 $a(MiAaPQ)EBC5925982 035 $a(PPN)243769024 035 $a(EXLCZ)994100000007158850 100 $a20181123d2019 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDealing with Imbalanced and Weakly Labelled Data in Machine Learning using Fuzzy and Rough Set Methods /$fby Sarah Vluymans 205 $a1st ed. 2019. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019. 215 $a1 online resource (XVIII, 249 p. 23 illus., 10 illus. in color.) 225 1 $aStudies in Computational Intelligence,$x1860-949X ;$v807 311 $a3-030-04662-1 327 $aIntroduction -- Classi?cation -- Understanding OWA based fuzzy rough sets -- Fuzzy rough set based classi?cation of semi-supervised data -- Multi-instance learning -- Multi-label learning -- Conclusions and future work -- Bibliography. 330 $aThis book presents novel classification algorithms for four challenging prediction tasks, namely learning from imbalanced, semi-supervised, multi-instance and multi-label data. The methods are based on fuzzy rough set theory, a mathematical framework used to model uncertainty in data. The book makes two main contributions: helping readers gain a deeper understanding of the underlying mathematical theory; and developing new, intuitive and well-performing classification approaches. The authors bridge the gap between the theoretical proposals of the mathematical model and important challenges in machine learning. The intended readership of this book includes anyone interested in learning more about fuzzy rough set theory and how to use it in practical machine learning contexts. Although the core audience chiefly consists of mathematicians, computer scientists and engineers, the content will also be interesting and accessible to students and professionals from a range of other fields. 410 0$aStudies in Computational Intelligence,$x1860-949X ;$v807 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 14$aComputational Intelligence. 615 24$aArtificial Intelligence. 676 $a511.3223 676 $a511.3223 700 $aVluymans$b Sarah$4aut$4http://id.loc.gov/vocabulary/relators/aut$01225886 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910483789203321 996 $aDealing with Imbalanced and Weakly Labelled Data in Machine Learning using Fuzzy and Rough Set Methods$92846217 997 $aUNINA