LEADER 03048nam 22005895 450 001 9910918598403321 005 20251113211300.0 010 $a9789819796229$b(electronic bk.) 010 $z9789819796212 024 7 $a10.1007/978-981-97-9622-9 035 $a(MiAaPQ)EBC31856122 035 $a(Au-PeEL)EBL31856122 035 $a(CKB)37077734100041 035 $a(OCoLC)1493056447 035 $a(DE-He213)978-981-97-9622-9 035 $a(EXLCZ)9937077734100041 100 $a20241223d2024 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMulti-objective, Multi-class and Multi-label Data Classification with Class Imbalance $eTheory and Practices /$fby Sanjay Chakraborty, Lopamudra Dey 205 $a1st ed. 2024. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2024. 215 $a1 online resource (177 pages) 225 1 $aSpringer Tracts in Nature-Inspired Computing,$x2524-5538 311 08$aPrint version: Chakraborty, Sanjay Multi-Objective, Multi-class and Multi-label Data Classification with Class Imbalance Singapore : Springer,c2025 9789819796212 327 $a1. Introduction to Classification -- 2. Class Imbalance and Data Irregularities in Classification -- 3. Multi-class Classification -- 4. Multi-Objective and Multi-Label Classification -- 5. Deep Learning Inspired Multiclass and Multilabel Classification -- 6. Applications of Multi-objective, Multi-label and Multi-class Classifications. 330 $aThis book explores intricate world of data classification with 'Multi-Objective, Multi-Class, and Multi-Label Data Classification.' This book studies sophisticated methods and strategies for working with complicated data sets, tackling the difficulties of various classes, many objectives, and complicated labelling tasks. This resource fosters a deeper grasp of multi-dimensional data analysis in today's data-driven world by providing readers with the skills and insights needed to navigate the subtleties of modern classification jobs, from algorithmic techniques to practical applications. 410 0$aSpringer Tracts in Nature-Inspired Computing,$x2524-5538 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aMachine learning 606 $aComputational Intelligence 606 $aArtificial Intelligence 606 $aMachine Learning 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 0$aMachine learning. 615 14$aComputational Intelligence. 615 24$aArtificial Intelligence. 615 24$aMachine Learning. 676 $a006.3 700 $aChakraborty$b Sanjay$01228885 701 $aDey$b Lopamudra$01781089 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9910918598403321 996 $aMulti-Objective, Multi-class and Multi-label Data Classification with Class Imbalance$94305883 997 $aUNINA