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Boosted Near-miss Under-sampling on SVM ensembles for concept detection in large-scale imbalanced datasets
Bao, Lei1,2; Juan, Cao1; Li, Jintao1; Zhang, Yongdong1
2016-01-08
发表期刊NEUROCOMPUTING
ISSN0925-2312
卷号172页码:198-206
摘要Considering the challenges of using SVM to learn concepts from large-scale imbalanced datasets, we proposed a new method: Boosted Near-miss Under-sampling on SVM ensembles (BNU-SVMs). The BNU-SVMs is under the framework of under-sampling ensemble method, where a sequence of SVMs is trained and the training dataset for each base SVM is selected by a Boosted Near-miss Under-sampling technique. More specifically, by adaptively updating weights over negative examples, the most near-miss negative examples in output space are selected in each iteration. Since the training dataset is balanced and reduced by under-sampling and the performance of classifier is improved by ensembles, the BNU-SVMs is a promising solution for large-scale and imbalance problem. Moreover, the negative examples selected by BNU-SVMs not only contain the most representative ones from data distribution perspective, but also cover the easily misclassified ones from data accuracy perspective. Therefore, the outperformance of the BNU-SVMs is expected. In addition, considering the computation cost caused by high-dimensional visual features, we proposed a kernel-distance pre-computation technique to further improve the efficiency of the BNU-SVMs. Experiments on TRECVID benchmark datasets show that the BNU-SVMs outperforms the previous methods significantly, which demonstrates that the BNU-SVMs is a both effective and efficient solution to concept detection in large-scale imbalanced datasets. (C) 2015 Published by Elsevier B.V.
关键词Concept learning Large-scale Imbalance Ensmeble learnning Support Vector Machine Boosted Near-miss Under-sampling
DOI10.1016/j.neucom.2014.05.096
收录类别SCI
语种英语
资助项目National High Technology Research and Development Program of China[2014AA015202] ; National Natural Science Foundation of China[61172153] ; National Natural Science Foundation of China[61100087] ; National Key Technology Research and Development Program of China[2012BAH39B02]
WOS研究方向Computer Science
WOS类目Computer Science, Artificial Intelligence
WOS记录号WOS:000364884700020
出版者ELSEVIER SCIENCE BV
引用统计
被引频次:50[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://119.78.100.204/handle/2XEOYT63/9140
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Zhang, Yongdong
作者单位1.Chinese Acad Sci, ICT, Lab Adv Comp Technol Res, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Grad Univ, Beijing 100049, Peoples R China
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Bao, Lei,Juan, Cao,Li, Jintao,et al. Boosted Near-miss Under-sampling on SVM ensembles for concept detection in large-scale imbalanced datasets[J]. NEUROCOMPUTING,2016,172:198-206.
APA Bao, Lei,Juan, Cao,Li, Jintao,&Zhang, Yongdong.(2016).Boosted Near-miss Under-sampling on SVM ensembles for concept detection in large-scale imbalanced datasets.NEUROCOMPUTING,172,198-206.
MLA Bao, Lei,et al."Boosted Near-miss Under-sampling on SVM ensembles for concept detection in large-scale imbalanced datasets".NEUROCOMPUTING 172(2016):198-206.
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