LEADER 03572nam 22005055 450 001 9910255019003321 005 20200705030633.0 010 $a3-319-41111-X 024 7 $a10.1007/978-3-319-41111-8 035 $a(CKB)3710000000837098 035 $a(DE-He213)978-3-319-41111-8 035 $a(MiAaPQ)EBC4635277 035 $a(PPN)194806820 035 $a(EXLCZ)993710000000837098 100 $a20160809d2016 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMultilabel Classification $eProblem Analysis, Metrics and Techniques /$fby Francisco Herrera, Francisco Charte, Antonio J. Rivera, Marķa J. del Jesus 205 $a1st ed. 2016. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2016. 215 $a1 online resource (XVI, 194 p. 72 illus.) 311 $a3-319-41110-1 320 $aIncludes bibliographical references at the end of each chapters. 327 $aIntroduction -- Multilabel Classification -- Case Studies and Metrics -- Transformation based Classifiers -- Adaptation based Classifiers -- Ensemble based Classifiers -- Dimensionality Reduction -- Imbalance in Multilabel Datasets -- Multilabel Software. 330 $aThis book offers a comprehensive review of multilabel techniques widely used to classify and label texts, pictures, videos and music in the Internet. A deep review of the specialized literature on the field includes the available software needed to work with this kind of data. It provides the user with the software tools needed to deal with multilabel data, as well as step by step instruction on how to use them. The main topics covered are: ? The special characteristics of multi-labeled data and the metrics available to measure them. ? The importance of taking advantage of label correlations to improve the results. ? The different approaches followed to face multi-label classification. ? The preprocessing techniques applicable to multi-label datasets. ? The available software tools to work with multi-label data. This book is beneficial for professionals and researchers in a variety of fields because of the wide range of potential applications for multilabel classification. Besides its multiple applications to classify different types of online information, it is also useful in many other areas, such as genomics and biology. No previous knowledge about the subject is required. The book introduces all the needed concepts to understand multilabel data characterization, treatment and evaluation. 606 $aData mining 606 $aArtificial intelligence 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 615 0$aData mining. 615 0$aArtificial intelligence. 615 14$aData Mining and Knowledge Discovery. 615 24$aArtificial Intelligence. 676 $a006.312 700 $aHerrera$b Francisco$4aut$4http://id.loc.gov/vocabulary/relators/aut$0426940 702 $aCharte$b Francisco$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aRivera$b Antonio J$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $adel Jesus$b Marķa J$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910255019003321 996 $aMultilabel Classification$91939210 997 $aUNINA