000			05480nam a2200565 i 4500
001			6542371
003			IEEE
005			20220712205850.0
006			m o d
007			cr |n|||||||||
008			151222s2013 njua ob 001 eng d
010			_z 2013019555 (print)
020			_a9781118646106 _qebook
020			_z9781118074626 _qprint
020			_z1118646207 _qelectronic
024	7		_a10.1002/9781118646106 _2doi
035			_a(CaBNVSL)mat06542371
035			_a(IDAMS)0b00006481da1ac4
040			_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL
050		4	_aQA76.9.D343 _bI43 2013eb
082	0	0	_a006.3/12 _223
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]
264		2	_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c[2013]
300			_a1 PDF (xi, 210 pages) : _billustrations.
336			_atext _2rdacontent
337			_aelectronic _2isbdmedia
338			_aonline resource _2rdacarrier
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. _93907
650		0	_aInformation resources _xEvaluation. _928237
650		0	_aSystem analysis _xMathematical models. _928238
650		0	_aInformation resources management. _97023
655		0	_aElectronic books. _93294
700	1		_aMa, Yunqian, _e�editeur intellectuel. _928239
700	1		_aHe, Haibo, _d1976-, _e�editeur intellectuel. _928240
710	2		_aIEEE Xplore (Online Service), _edistributor. _928241
710	2		_aWiley, _epublisher. _928242
776	0	8	_iPrint version: _z9781118074626
856	4	2	_3Abstract with links to resource _uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6542371
942			_cEBK
999			_c74310 _d74310