LEADER 05422nam 2200673 450 001 9910141572403321 005 20221206103809.0 010 $a1-118-64633-9 010 $a1-118-64620-7 024 7 $a10.1002/9781118646106 035 $a(CKB)2670000000360077 035 $a(EBL)1204742 035 $a(SSID)ssj0000886212 035 $a(PQKBManifestationID)11487395 035 $a(PQKBTitleCode)TC0000886212 035 $a(PQKBWorkID)10816368 035 $a(PQKB)10497643 035 $a(MiAaPQ)EBC1204742 035 $a(DLC) 2013019555 035 $a(CaBNVSL)mat06542371 035 $a(IDAMS)0b00006481da1ac4 035 $a(IEEE)6542371 035 $a(OCoLC)843228806 035 $a(EXLCZ)992670000000360077 100 $a20151222d2013 uy 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aImbalanced learning $efoundations, algorithms, and applications /$fedited by Haibo He, Yunqian Ma 210 1$aPiscataway, NJ :$cIEEE Press ;$aHoboken, New Jersey :$cWiley,$d[2013] 210 2$a[Piscataqay, New Jersey] :$cIEEE Xplore,$d[2013] 215 $a1 online resource (224 p.) 300 $aDescription based upon print version of record. 311 $a1-118-64610-X 311 $a1-118-07462-9 320 $aIncludes bibliographical references and index. 327 $aPreface ix -- Contributors xi -- 1 Introduction 1 -- Haibo He -- 1.1 Problem Formulation, 1 -- 1.2 State-of-the-Art Research, 3 -- 1.3 Looking Ahead: Challenges and Opportunities, 6 -- 1.4 Acknowledgments, 7 -- References, 8 -- 2 Foundations of Imbalanced Learning 13 -- Gary M. Weiss -- 2.1 Introduction, 14 -- 2.2 Background, 14 -- 2.3 Foundational Issues, 19 -- 2.4 Methods for Addressing Imbalanced Data, 26 -- 2.5 Mapping Foundational Issues to Solutions, 35 -- 2.6 Misconceptions About Sampling Methods, 36 -- 2.7 Recommendations and Guidelines, 38 -- References, 38 -- 3 Imbalanced Datasets: From Sampling to Classifiers 43 -- T. Ryan Hoens and Nitesh V. Chawla -- 3.1 Introduction, 43 -- 3.2 Sampling Methods, 44 -- 3.3 Skew-Insensitive Classifiers for Class Imbalance, 49 -- 3.4 Evaluation Metrics, 52 -- 3.5 Discussion, 56 -- References, 57 -- 4 Ensemble Methods for Class Imbalance Learning 61 -- Xu-Ying Liu and Zhi-Hua Zhou -- 4.1 Introduction, 61 -- 4.2 Ensemble Methods, 62 -- 4.3 Ensemble Methods for Class Imbalance Learning, 66 -- 4.4 Empirical Study, 73 -- 4.5 Concluding Remarks, 79 -- References, 80 -- 5 Class Imbalance Learning Methods for Support Vector Machines 83 -- Rukshan Batuwita and Vasile Palade -- 5.1 Introduction, 83 -- 5.2 Introduction to Support Vector Machines, 84 -- 5.3 SVMs and Class Imbalance, 86 -- 5.4 External Imbalance Learning Methods for SVMs: Data Preprocessing Methods, 87 -- 5.5 Internal Imbalance Learning Methods for SVMs: Algorithmic Methods, 88 -- 5.6 Summary, 96 -- References, 96 -- 6 Class Imbalance and Active Learning 101 -- Josh Attenberg and Sd eyda Ertekin -- 6.1 Introduction, 102 -- 6.2 Active Learning for Imbalanced Problems, 103 -- 6.3 Active Learning for Imbalanced Data Classification, 110 -- 6.4 Adaptive Resampling with Active Learning, 122 -- 6.5 Difficulties with Extreme Class Imbalance, 129 -- 6.6 Dealing with Disjunctive Classes, 130 -- 6.7 Starting Cold, 132 -- 6.8 Alternatives to Active Learning for Imbalanced Problems, 133. 327 $a6.9 Conclusion, 144 -- References, 145 -- 7 Nonstationary Stream Data Learning with Imbalanced Class Distribution 151 -- Sheng Chen and Haibo He -- 7.1 Introduction, 152 -- 7.2 Preliminaries, 154 -- 7.3 Algorithms, 157 -- 7.4 Simulation, 167 -- 7.5 Conclusion, 182 -- 7.6 Acknowledgments, 183 -- References, 184 -- 8 Assessment Metrics for Imbalanced Learning 187 -- Nathalie Japkowicz -- 8.1 Introduction, 187 -- 8.2 A Review of Evaluation Metric Families and their Applicability -- to the Class Imbalance Problem, 189 -- 8.3 Threshold Metrics: Multiple- Versus Single-Class Focus, 190 -- 8.4 Ranking Methods and Metrics: Taking Uncertainty into Consideration, 196 -- 8.5 Conclusion, 204 -- 8.6 Acknowledgments, 205 -- References, 205 -- Index 207. 330 $aSolving imbalanced learning problems is critical in numerous data-intensive networked systems, including surveillance, security, Internet, finance, biomedical, and defense, to name a few. The first comprehensive look at this new branch of machine learning, this volume offers a critical review of the problem of imbalanced learning, covering the state-of-the-art in techniques, principles, and real-world applications. Scientists and engineers will learn how to tackle the problem of learning from imbalanced datasets, and gain insight into current developments in the field as well as future research.--[Source inconnue] 606 $aData mining 606 $aInformation resources management 606 $aInformation resources$xEvaluation 606 $aSystem analysis$xMathematical models 615 0$aData mining. 615 0$aInformation resources management. 615 0$aInformation resources$xEvaluation. 615 0$aSystem analysis$xMathematical models. 676 $a006.312 702 $aMa$b Yunqian 702 $aHe$b Haibo$f1976-, 801 0$bCaBNVSL 801 1$bCaBNVSL 801 2$bCaBNVSL 906 $aBOOK 912 $a9910141572403321 996 $aImbalanced learning$92225009 997 $aUNINA LEADER 00871nam a2200253 i 4500 001 991001605099707536 005 20020503123522.0 008 990916s1975 uk ||| | eng 020 $a0701120657 035 $ab10245248-39ule_inst 035 $aLE01281086$9ExL 040 $aDip.to Lingue$bita 100 1 $aBlack, Michael$063188 245 14$aThe literature of fidelity /$cMichael Black 260 $aLondon :$bChatto & Windus,$cc1975 300 $a216 p. ;$c23 cm. 650 4$aAmore nella letteratura 650 4$aMatrimonio nella letteratura 907 $a.b10245248$b21-09-06$c27-06-02 912 $a991001605099707536 945 $aLE012 809.933 54 BLA$g1$i2012000011496$lle012$o-$pE0.00$q-$rl$s- $t0$u2$v0$w2$x0$y.i10295410$z27-06-02 996 $aLiterature of fidelity$9206624 997 $aUNISALENTO 998 $ale012$b01-01-99$cm$da $e-$feng$guk $h4$i1