Computer Engineering and Applications ›› 2018, Vol. 54 ›› Issue (2): 149-154.DOI: 10.3778/j.issn.1002-8331.1608-0140

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MIML algorithm E-MIMLSVM+ based on semi-supervised learning

LI Cunhe, ZHU Hongbo   

  1. School of Computer and Communication Engineering, China University of Petroleum, Qingdao, Shandong 266580, China
  • Online:2018-01-15 Published:2018-01-31



  1. 中国石油大学(华东) 计算机与通信工程学院,山东 青岛 266580

Abstract: Multi-instance multi-label learning is a new machine learning framework. In MIML framework, an example is described by multiple instances and associated with multiple class labels. Algorithm MIMLSVM+ decomposes the MIML problem into multiple independent binary classification problems. However, the degeneration process may lose information, which will influence the classification performance. Algorithm E-MIMLSVM+ by using multitask learning techniques is utilized to incorporate label correlations to improve the algorithm MIMLSVM+. In order to make full use of the unlabeled samples to improve the classification accuracy, this paper improves the E-MIMLSVM+ algorithm by using the semi-supervised support vector machine TSVM. In this paper, the algorithm is compared with other MIML algorithms. The experimental results show that the algorithm has achieved good classification results.

Key words: machine learning, Multi-Instance Multi-Label(MIML), Support Vector Machine(SVM), semi-supervised learning

摘要: 多示例多标记是一种新的机器学习框架,在该框架下一个对象用多个示例来表示,同时与多个类别标记相关联。MIMLSVM+算法将多示例多标记问题转化为一系列独立的二类分类问题,但是在退化过程中标记之间的联系信息会丢失,而E-MIMLSVM+算法则通过引入多任务学习技术对MIMLSVM+算法进行了改进。为了充分利用未标记样本来提高分类准确率,使用半监督支持向量机TSVM对E-MIMLSVM+算法进行了改进。通过实验将该算法与其他多示例多标记算法进行了比较,实验结果显示,改进算法取得了良好的分类效果。

关键词: 机器学习, 多示例多标记, 支持向量机(SVM), 半监督学习