000 | 05400nam a2200553 i 4500 | ||
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001 | 6542371 | ||
003 | IEEE | ||
005 | 20230927112355.0 | ||
006 | m o d | ||
007 | cr |n||||||||| | ||
008 | 151222s2013 njua ob 001 eng d | ||
010 | _z 2013019555 (print) | ||
020 |
_a9781118646106 _qebook |
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020 |
_z9781118074626 _qprint |
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020 |
_z1118646207 _qelectronic |
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024 | 7 |
_a10.1002/9781118646106 _2doi |
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035 | _a(CaBNVSL)mat06542371 | ||
035 | _a(IDAMS)0b00006481da1ac4 | ||
040 |
_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL |
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082 | 0 | 0 | _a006.3/12 |
245 | 0 | 0 |
_aImbalanced learning : _bfoundations, algorithms, and applications / _cedited by Haibo He, Yunqian Ma. |
264 | 1 |
_aPiscataway, NJ : _bIEEE Press ; _aHoboken, New Jersey : _bWiley, _c[2013] |
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264 | 2 |
_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c[2013] |
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300 |
_a1 PDF (xi, 210 pages) : _billustrations. |
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336 |
_atext _2rdacontent |
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337 |
_aelectronic _2isbdmedia |
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338 |
_aonline resource _2rdacarrier |
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500 | _aIn Wiley online library | ||
504 | _aIncludes bibliographical references. | ||
505 | 0 | _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. | |
505 | 8 | _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. | |
506 | 1 | _aRestricted to subscribers or individual electronic text purchasers. | |
520 | _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] | ||
530 | _aAlso available in print. | ||
538 | _aMode of access: World Wide Web | ||
588 | _aDescription based on PDF viewed 12/22/2015. | ||
650 | 0 | _aData mining. | |
650 | 0 |
_aInformation resources _xEvaluation. |
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650 | 0 |
_aSystem analysis _xMathematical models. |
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650 | 0 | _aInformation resources management. | |
655 | 0 | _aElectronic books. | |
700 | 1 |
_aMa, Yunqian, _e�editeur intellectuel. |
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700 | 1 |
_aHe, Haibo, _d1976-, _e�editeur intellectuel. |
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710 | 2 |
_aIEEE Xplore (Online Service), _edistributor. |
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710 | 2 |
_aWiley, _epublisher. |
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776 | 0 | 8 |
_iPrint version: _z9781118074626 |
856 | 4 | 2 |
_3Abstract with links to resource _uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6542371 |
999 |
_c40573 _d40573 |