Dynamic Synthetic Minority Over-Sampling Technique-Based Rotation Forest for the Classification of Imbalanced Hyperspectral Data
文献类型：期刊论文
| 作者 | Feng, Wei7; Dauphin, Gabriel6; Huang, Wenjiang; Quan, Yinghui5; Bao, Wenxing4; Wu, Mingquan3; Li, Qiang2 |
| 刊名 | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
 |
| 出版日期 | 2019 |
| 卷号 | 12期号:7页码:2159-2169 |
| 关键词 | ENSEMBLES SELECTION SMOTE |
| ISSN号 | 1939-1404 |
| DOI | 10.1109/JSTARS.2019.2922297 |
| 英文摘要 | Rotation forest (RoF) is a powerful ensemble classifier and has attracted substantial attention due to its performance in hyperspectral data classification. Multi-class imbalance learning is one of the biggest challenges in machine learning and remote sensing. The standard technique for constructing RoF ensemble tends to increase the overall accuracy; RoF has difficulty to sufficiently recognize the minority class. This paper proposes a novel dynamic SMOTE (synthetic minority oversampling technique)-based RoF algorithm for the multi-class imbalance problem. The main idea of the proposed method is to dynamically balance the class distribution before building each rotation decision tree. A resampling rate is set in each iteration (ranging from 10% in the first iteration to 100% in the last) and this ratio defines the number of minority class instances randomly resampled (with replacement) from the original dataset in each iteration. The rest of the minority class instances are generated by the SMOTE method. The reported results on three real hyperspectral datasets show that the proposed method can get better performance than random forest, RoF, and some popular data sampling methods. |
| 学科主题 | Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| 源URL | [http://ir.itp.ac.cn/handle/311006/27215]  |
| 专题 | 理论物理研究所_理论物理所1978-2010年知识产出 |
| 作者单位 | 1.Chinese Acad Sci, Inst Theoret Phys, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China 3.Beifang Univ Nationalities, Sch Comp Sci & Engn, Yinchuan 750021, Ningxia, Peoples R China 4.Xidian Univ, Key Lab Radar Signal Proc, Xian 710071, Shaanxi, Peoples R China 5.Univ Paris 13, Inst Galilee, L2TI, Lab Informat Proc & Transmiss, F-93430 Villetaneuse, France 6.Xidian Univ, Sch Elect Engn, Xian 710071, Peoples R China 7.Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China |
| 推荐引用方式 GB/T 7714 | Feng, Wei,Dauphin, Gabriel,Huang, Wenjiang,et al. Dynamic Synthetic Minority Over-Sampling Technique-Based Rotation Forest for the Classification of Imbalanced Hyperspectral Data[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2019,12(7):2159-2169. |
| APA | Feng, Wei.,Dauphin, Gabriel.,Huang, Wenjiang.,Quan, Yinghui.,Bao, Wenxing.,...&Li, Qiang.(2019).Dynamic Synthetic Minority Over-Sampling Technique-Based Rotation Forest for the Classification of Imbalanced Hyperspectral Data.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,12(7),2159-2169. |
| MLA | Feng, Wei,et al."Dynamic Synthetic Minority Over-Sampling Technique-Based Rotation Forest for the Classification of Imbalanced Hyperspectral Data".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 12.7(2019):2159-2169. |
入库方式： OAI收割
来源：理论物理研究所
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