期刊
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
卷 27, 期 12, 页码 3347-3359出版社
IEEE COMPUTER SOC
DOI: 10.1109/TKDE.2015.2453171
关键词
Machine learning; kernel-based learning; imbalanced classification; ranking loss; large-scale algorithms
类别
资金
- National Sciences and Engineering Research Council of Canada
- Ophelia Lazaridis University Research Chair
Rare class problems are common in real-world applications across a wide range of domains. Standard classification algorithms are known to perform poorly in these cases, since they focus on overall classification accuracy. In addition, we have seen a significant increase of data in recent years, resulting in many large scale rare class problems. In this paper, we focus on nonlinear kernel based classification methods expressed as a regularized loss minimization problem. We address the challenges associated with both rare class problems and large scale learning, by 1) optimizing area under curve of the receiver of operator characteristic in the training process, instead of classification accuracy and 2) using a rare class kernel representation to achieve an efficient time and space algorithm. We call the algorithm RankRC. We provide justifications for the rare class representation and experimentally illustrate the effectiveness of RankRC in test performance, computational complexity, and model robustness.
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