LEADER 05057nam 22005775 450 001 9910299357903321 005 20210616234016.0 010 $a3-319-98074-2 024 7 $a10.1007/978-3-319-98074-4 035 $a(CKB)4100000007102901 035 $a(MiAaPQ)EBC5566807 035 $a(DE-He213)978-3-319-98074-4 035 $a(PPN)231464533 035 $a(EXLCZ)994100000007102901 100 $a20181022d2018 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 10$aLearning from Imbalanced Data Sets /$fby Alberto Fernández, Salvador García, Mikel Galar, Ronaldo C. Prati, Bartosz Krawczyk, Francisco Herrera 205 $a1st ed. 2018. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018. 215 $a1 online resource (385 pages) $cillustrations (some color) 311 $a3-319-98073-4 320 $aIncludes bibliographical references. 327 $a1 Introduction to KDD and Data Science -- 2 Foundations on Imbalanced Classification -- 3 Performance measures -- 4 Cost-sensitive Learning -- 5 Data Level Preprocessing Methods -- 6 Algorithm-level Approaches -- 7 Ensemble Learning -- 8 Imbalanced Classification with Multiple Classes -- 9 Dimensionality Reduction for Imbalanced Learning -- 10 Data Intrinsic Characteristics -- 11 Learning from Imbalanced Data Streams -- 12 Non-Classical Imbalanced Classification Problems -- 13 Imbalanced Classification for Big Data -- 14 Software and Libraries for Imbalanced Classification. . 330 $aThis book provides a general and comprehensible overview of imbalanced learning. It contains a formal description of a problem, and focuses on its main features, and the most relevant proposed solutions. Additionally, it considers the different scenarios in Data Science for which the imbalanced classification can create a real challenge. This book stresses the gap with standard classification tasks by reviewing the case studies and ad-hoc performance metrics that are applied in this area. It also covers the different approaches that have been traditionally applied to address the binary skewed class distribution. Specifically, it reviews cost-sensitive learning, data-level preprocessing methods and algorithm-level solutions, taking also into account those ensemble-learning solutions that embed any of the former alternatives. Furthermore, it focuses on the extension of the problem for multi-class problems, where the former classical methods are no longer to be applied in a straightforward way. This book also focuses on the data intrinsic characteristics that are the main causes which, added to the uneven class distribution, truly hinders the performance of classification algorithms in this scenario. Then, some notes on data reduction are provided in order to understand the advantages related to the use of this type of approaches. Finally this book introduces some novel areas of study that are gathering a deeper attention on the imbalanced data issue. Specifically, it considers the classification of data streams, non-classical classification problems, and the scalability related to Big Data. Examples of software libraries and modules to address imbalanced classification are provided. This book is highly suitable for technical professionals, senior undergraduate and graduate students in the areas of data science, computer science and engineering. It will also be useful for scientists and researchers to gain insight on the current developments in this area of study, as well as future research directions. . 606 $aArtificial intelligence 606 $aComputers 606 $aComputer communication systems 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aInformation Systems and Communication Service$3https://scigraph.springernature.com/ontologies/product-market-codes/I18008 606 $aComputer Communication Networks$3https://scigraph.springernature.com/ontologies/product-market-codes/I13022 615 0$aArtificial intelligence. 615 0$aComputers. 615 0$aComputer communication systems. 615 14$aArtificial Intelligence. 615 24$aInformation Systems and Communication Service. 615 24$aComputer Communication Networks. 676 $a006.3 700 $aFernández$b Alberto$c(Computer scientist).$4aut$4http://id.loc.gov/vocabulary/relators/aut$0935062 702 $aGarcía$b Salvador$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aGalar$b Mikel$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aPrati$b Ronaldo C$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aKrawczyk$b Bartosz$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aHerrera$b Francisco$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910299357903321 996 $aLearning from Imbalanced Data Sets$92105733 997 $aUNINA