Discussion of Classification for Imbalanced Data Sets

Abstract:

Most classifiers lose efficiency with the problem of imbalanced class distribution, which, however, often shows statistical significant in practice. Therefore, the problem of learning from imbalanced datasets has attracted growing attention in recent years. The paper provide a comprehensive review of the classification of imbalanced datasets, the nature of the problem, the factor which affected the problem, the current assessment metrics used to evaluate learning performance, as well as the opportunities and challenges in the learning from imbalanced data.

• Periodical: Advanced Materials Research (Volumes 546-547)
• Pages: 622-627
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.546-547.622
• Online since: July 2012
• Authors: Wei Mei Zhi, Hua Ping Guo, Ming Fan
• Keywords: Classification, Cost-Sensitive Learning, Imbalanced Data Sets, Sampling Methods

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