{"product_id":"imbalanced-learning-9781118074626","title":"Imbalanced Learning","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eThe first book of its kind to review the current status and future direction of the exciting new branch of machine learning\/data mining called imbalanced learning\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eImbalanced learning focuses on how an intelligent system can learn when it is provided with imbalanced data. Solving imbalanced learning problems is critical in numerous data-intensive networked systems, including surveillance, security, Internet, finance, biomedical, defense, and more. Due to the inherent complex characteristics of imbalanced data sets, learning from such data requires new understandings, principles, algorithms, and tools to transform vast amounts of raw data efficiently into information and knowledge representation.\u003c\/p\u003e \u003cp\u003eThe first comprehensive look at this new branch of machine learning, this book offers a critical review of the problem of imbalanced learning, covering the state of the art in techniques, principles, and real-world applications. Featuring contributions from experts in bot\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e“This book certainly qualifies as a reference for graduate studies in machine learning. Research students are sure to find it highly valuable and a prized possession, especially taking into account the wealth of supporting literature that the authors have brought to the fore.”  (\u003ci\u003eComputing Reviews\u003c\/i\u003e, 27 March 2014)\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePreface ix\u003c\/p\u003e \u003cp\u003eContributors xi\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction 1\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eHaibo He\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Problem Formulation 1\u003c\/p\u003e \u003cp\u003e1.2 State-of-the-Art Research 3\u003c\/p\u003e \u003cp\u003e1.3 Looking Ahead: Challenges and Opportunities 6\u003c\/p\u003e \u003cp\u003e1.4 Acknowledgments 7\u003c\/p\u003e \u003cp\u003eReferences 8\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Foundations of Imbalanced Learning 13\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eGary M. Weiss\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 14\u003c\/p\u003e \u003cp\u003e2.2 Background 14\u003c\/p\u003e \u003cp\u003e2.3 Foundational Issues 19\u003c\/p\u003e \u003cp\u003e2.4 Methods for Addressing Imbalanced Data 26\u003c\/p\u003e \u003cp\u003e2.5 Mapping Foundational Issues to Solutions 35\u003c\/p\u003e \u003cp\u003e2.6 Misconceptions About Sampling Methods 36\u003c\/p\u003e \u003cp\u003e2.7 Recommendations and Guidelines 38\u003c\/p\u003e \u003cp\u003eReferences 38\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Imbalanced Datasets: From Sampling to Classifiers 43\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eT. Ryan Hoens and Nitesh V. Chawla\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 43\u003c\/p\u003e \u003cp\u003e3.2 Sampling Methods 44\u003c\/p\u003e \u003cp\u003e3.3 Skew-Insensitive Classifiers for Class Imbalance 49\u003c\/p\u003e \u003cp\u003e3.4 Evaluation Metrics 52\u003c\/p\u003e \u003cp\u003e3.5 Discussion 56\u003c\/p\u003e \u003cp\u003eReferences 57\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Ensemble Methods for Class Imbalance Learning 61\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eXu-Ying Liu and Zhi-Hua Zhou\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 61\u003c\/p\u003e \u003cp\u003e4.2 Ensemble Methods 62\u003c\/p\u003e \u003cp\u003e4.3 Ensemble Methods for Class Imbalance Learning 66\u003c\/p\u003e \u003cp\u003e4.4 Empirical Study 73\u003c\/p\u003e \u003cp\u003e4.5 Concluding Remarks 79\u003c\/p\u003e \u003cp\u003eReferences 80\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Class Imbalance Learning Methods for Support Vector Machines 83\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eRukshan Batuwita and Vasile Palade\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 83\u003c\/p\u003e \u003cp\u003e5.2 Introduction to Support Vector Machines 84\u003c\/p\u003e \u003cp\u003e5.3 SVMs and Class Imbalance 86\u003c\/p\u003e \u003cp\u003e5.4 External Imbalance Learning Methods for SVMs: Data Preprocessing Methods 87\u003c\/p\u003e \u003cp\u003e5.5 Internal Imbalance Learning Methods for SVMs: Algorithmic Methods 88\u003c\/p\u003e \u003cp\u003e5.6 Summary 96\u003c\/p\u003e \u003cp\u003eReferences 96\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Class Imbalance and Active Learning 101\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eJosh Attenberg and Seyda Ertekin\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 102\u003c\/p\u003e \u003cp\u003e6.2 Active Learning for Imbalanced Problems 103\u003c\/p\u003e \u003cp\u003e6.3 Active Learning for Imbalanced Data Classification 110\u003c\/p\u003e \u003cp\u003e6.4 Adaptive Resampling with Active Learning 122\u003c\/p\u003e \u003cp\u003e6.5 Difficulties with Extreme Class Imbalance 129\u003c\/p\u003e \u003cp\u003e6.6 Dealing with Disjunctive Classes 130\u003c\/p\u003e \u003cp\u003e6.7 Starting Cold 132\u003c\/p\u003e \u003cp\u003e6.8 Alternatives to Active Learning for Imbalanced Problems 133\u003c\/p\u003e \u003cp\u003e6.9 Conclusion 144\u003c\/p\u003e \u003cp\u003eReferences 145\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Nonstationary Stream Data Learning with Imbalanced Class Distribution 151\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSheng Chen and Haibo He\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 152\u003c\/p\u003e \u003cp\u003e7.2 Preliminaries 154\u003c\/p\u003e \u003cp\u003e7.3 Algorithms 157\u003c\/p\u003e \u003cp\u003e7.4 Simulation 167\u003c\/p\u003e \u003cp\u003e7.5 Conclusion 182\u003c\/p\u003e \u003cp\u003e7.6 Acknowledgments 183\u003c\/p\u003e \u003cp\u003eReferences 184\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Assessment Metrics for Imbalanced Learning 187\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eNathalie Japkowicz\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 187\u003c\/p\u003e \u003cp\u003e8.2 A Review of Evaluation Metric Families and their Applicability to the Class Imbalance Problem 189\u003c\/p\u003e \u003cp\u003e8.3 Threshold Metrics: Multiple- Versus Single-Class Focus 190\u003c\/p\u003e \u003cp\u003e8.4 Ranking Methods and Metrics: Taking Uncertainty into Consideration 196\u003c\/p\u003e \u003cp\u003e8.5 Conclusion 204\u003c\/p\u003e \u003cp\u003e8.6 Acknowledgments 205\u003c\/p\u003e \u003cp\u003eReferences 205\u003c\/p\u003e \u003cp\u003eIndex 207\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49406826873175,"sku":"9781118074626","price":97.16,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781118074626.jpg?v=1730497251","url":"https:\/\/bookcurl.com\/products\/imbalanced-learning-9781118074626","provider":"Book Curl","version":"1.0","type":"link"}