The first booklet of its style to study the present prestige and destiny path of the intriguing new department of computer learning/data mining referred to as imbalanced learning
Imbalanced studying specializes in how an clever procedure can study while it really is supplied with imbalanced information. fixing imbalanced studying difficulties is necessary in different data-intensive networked structures, together with surveillance, defense, net, finance, biomedical, security, and extra. as a result of the inherent advanced features of imbalanced info units, studying from such info calls for new understandings, ideas, algorithms, and instruments to remodel great quantities of uncooked facts successfully into info and data illustration.
The first entire examine this new department of computing device studying, this publication deals a serious evaluate of the matter of imbalanced studying, protecting the state-of-the-art in recommendations, rules, and real-world functions. that includes contributions from specialists in either academia and undefined, Imbalanced studying: Foundations, Algorithms, and Applications presents bankruptcy assurance on:
- Foundations of Imbalanced Learning
- Imbalanced Datasets: From Sampling to Classifiers
- Ensemble equipment for sophistication Imbalance Learning
- Class Imbalance studying tools for help Vector Machines
- Class Imbalance and lively Learning
- Nonstationary flow facts studying with Imbalanced category Distribution
- Assessment Metrics for Imbalanced Learning
Imbalanced studying: Foundations, Algorithms, and Applications might help scientists and engineers the way to take on the matter of studying from imbalanced datasets, and achieve perception into present advancements within the box in addition to destiny learn directions.Content:
Chapter 1 creation (pages 1–12): Haibo He
Chapter 2 Foundations of Imbalanced studying (pages 13–41): Gary M. Weiss
Chapter three Imbalanced Datasets: From Sampling to Classifiers (pages 43–59): T. Ryan Hoens and Nitesh V. Chawla
Chapter four Ensemble equipment for sophistication Imbalance studying (pages 61–82): Xu?Ying Liu and Zhi?Hua Zhou
Chapter five category Imbalance studying equipment for aid Vector Machines (pages 83–99): Rukshan Batuwita and Vasile Palade
Chapter 6 type Imbalance and lively studying (pages 101–149): Josh Attenberg and Seyda Ertekin
Chapter 7 Nonstationary flow info studying with Imbalanced type Distribution (pages 151–186): Sheng Chen and Haibo He
Chapter eight review Metrics for Imbalanced studying (pages 187–206): Nathalie Japkowicz
Read or Download Imbalanced Learning: Foundations, Algorithms, and Applications PDF
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Additional resources for Imbalanced Learning: Foundations, Algorithms, and Applications
24. H. Yao, H. Hamilton, and C. Butz, “A foundational approach to mining itemset utilities from databases,” in Proceedings of the Fourth SIAM International Conference on Data Mining (Lake Buena Vista, FL, USA), pp. 482–496, Society for Industrial and Applied Mathematics (SIAM), 2004. 25. J. Yao and J. Hamilton, “Mining itemset utilities from transaction databases,” Data and Knowledge Engineering, vol. 59, no. 3, pp. 603–626, 2006. 26. Y. Shen, Q. Yang, and Z.
N. Japkowicz, “Concept learning in the presence of between-class and within-class imbalances,” in Proceedings of the Fourteenth Conference of the Canadian Society for Computational Studies of Intelligence (Ottawa, Canada), pp. 67–77, Springer-Verlag, 2001. REFERENCES 39 6. N. Japkowicz, “Class imbalances versus small disjuncts,” ACM SIGKDD Explorations, vol. 6, no. 1, pp. 40–49, 2004. 7. G. Weiss, “Learning with rare cases and small disjuncts,” in Proceedings of the Twelfth International Conference on Machine Learning (Tahoe City, CA, USA), pp.
This is a very difficult issue to address, but methods for doing this are described in this section. This section also describes methods for dealing with relative rarity (the standard class imbalance problem), even though, as we shall discuss, we believe that issues with relative rarity are best addressed at the algorithms level. 1 Active Learning and Other Information Acquisition Strategies The most direct way of addressing the issue of absolute rarity is to acquire additional labeled training data.