Imbalanced learning : (Record no. 59902)
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000 -LEADER | |
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fixed length control field | 05419nam a2200565 i 4500 |
001 - CONTROL NUMBER | |
control field | 6542371 |
005 - DATE AND TIME OF LATEST TRANSACTION | |
control field | 20200421114528.0 |
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
fixed length control field | 151222s2013 njua ob 001 eng d |
020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
ISBN | 9781118646106 |
-- | ebook |
020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
-- | |
020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
-- | electronic |
082 00 - CLASSIFICATION NUMBER | |
Call Number | 006.3/12 |
245 00 - TITLE STATEMENT | |
Title | Imbalanced learning : |
Sub Title | foundations, algorithms, and applications / |
300 ## - PHYSICAL DESCRIPTION | |
Number of Pages | 1 PDF (xi, 210 pages) : |
500 ## - GENERAL NOTE | |
Remark 1 | In Wiley online library |
505 0# - FORMATTED CONTENTS NOTE | |
Remark 2 | Preface 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# - FORMATTED CONTENTS NOTE | |
Remark 2 | 6.9 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. |
520 ## - SUMMARY, ETC. | |
Summary, etc | Solving 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] |
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
General subdivision | Evaluation. |
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
General subdivision | Mathematical models. |
700 1# - AUTHOR 2 | |
Author 2 | Ma, Yunqian, |
700 1# - AUTHOR 2 | |
Author 2 | He, Haibo, |
856 42 - ELECTRONIC LOCATION AND ACCESS | |
Uniform Resource Identifier | http://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6542371 |
942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
Koha item type | eBooks |
264 #1 - | |
-- | Piscataway, NJ : |
-- | IEEE Press ; |
-- | Hoboken, New Jersey : |
-- | Wiley, |
-- | [2013] |
264 #2 - | |
-- | [Piscataqay, New Jersey] : |
-- | IEEE Xplore, |
-- | [2013] |
336 ## - | |
-- | text |
-- | rdacontent |
337 ## - | |
-- | electronic |
-- | isbdmedia |
338 ## - | |
-- | online resource |
-- | rdacarrier |
588 ## - | |
-- | Description based on PDF viewed 12/22/2015. |
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Data mining. |
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Information resources |
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | System analysis |
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Information resources management. |
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