A New Strategy of Cost-Free Learning in the Class Imbalance Problem
文献类型：期刊论文
| 作者 | Zhang, Xiaowan; Hu, Bao-Gang
|
| 刊名 | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
 |
| 出版日期 | 2014-12-01 |
| 卷号 | 26期号:12页码:2872-2885 |
| 关键词 | Classification class imbalance cost-free learning cost-sensitive learning abstaining mutual information ROC |
| 英文摘要 | In this work, we define cost-free learning (CFL) formally in comparison with cost-sensitive learning (CSL). The main difference between them is that a CFL approach seeks optimal classification results without requiring any cost information, even in the class imbalance problem. In fact, several CFL approaches exist in the related studies, such as sampling and some criteria-based approaches. However, to our best knowledge, none of the existing CFL and CSL approaches are able to process the abstaining classifications properly when no information is given about errors and rejects. Based on information theory, we propose a novel CFL which seeks to maximize normalized mutual information of the targets and the decision outputs of classifiers. Using the strategy, we can handle binary/multi-class classifications with/without abstaining. Significant features are observed from the new strategy. While the degree of class imbalance is changing, the proposed strategy is able to balance the errors and rejects accordingly and automatically. Another advantage of the strategy is its ability of deriving optimal rejection thresholds for abstaining classifications and the "equivalent" costs in binary classifications. The connection between rejection thresholds and ROC curve is explored. Empirical investigation is made on several benchmark data sets in comparison with other existing approaches. The classification results demonstrate a promising perspective of the strategy in machine learning. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic |
| 研究领域[WOS] | Computer Science ; Engineering |
| 关键词[WOS] | NEURAL-NETWORKS ; REJECT-OPTION ; CLASSIFICATION ; CLASSIFIERS ; TREES ; RECOGNITION ; PERFORMANCE ; ROBUST |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000344993900002 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/2843]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_多媒体计算与图形学团队 |
| 作者单位 | Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Xiaowan,Hu, Bao-Gang. A New Strategy of Cost-Free Learning in the Class Imbalance Problem[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2014,26(12):2872-2885. |
| APA | Zhang, Xiaowan,&Hu, Bao-Gang.(2014).A New Strategy of Cost-Free Learning in the Class Imbalance Problem.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,26(12),2872-2885. |
| MLA | Zhang, Xiaowan,et al."A New Strategy of Cost-Free Learning in the Class Imbalance Problem".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 26.12(2014):2872-2885. |
入库方式： OAI收割
来源：自动化研究所
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