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计算机系统应用:2018,27(8):146-152
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基于标记相关性的多示例多标记算法
李村合, 田程程, 姜宇
(中国石油大学(华东)计算机与通信工程学院, 青岛 266580)
Multi-Instance Multi-Label Algorithm Based on Label Correlation
LI Cun-He, TIAN Cheng-Cheng, JIANG Yu
(College of Computer & Communication Engineering, China University of Petroleum(East China), Qingdao 266580, China)
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投稿时间:2017-12-26    修订日期:2018-01-16
中文摘要: 多示例多标记学习(Multi-Instance Multi-Label,MIML)是一种新的机器学习框架,基于该框架上的样本由多个示例组成并且与多个类别相关联,该框架因其对多义性对象具有出色的表达能力,已成为机器学习界研究的热点.解决MIML分类问题的最直接的思路是采用退化策略,通过向多示例学习或多标记学习的退化,将MIML框架下的分类问题简化为一系列的二类分类问题进行求解.但是在退化过程中会丢失标记之间的关联信息,降低分类的准确率.针对此问题,本文提出了MIMLSVM-LOC算法,该算法将改进的MIMLSVM算法与一种局部标记相关性的方法ML-LOC相结合,在训练过程中结合标记之间的关联信息进行分类.算法首先对MIMLSVM算法中的K-medoids聚类算法进行改进,采用的混合Hausdorff距离,将每一个示例包转化为一个示例,将MIML问题进行了退化.然后采用单示例多标记的算法ML-LOC算法继续以后的分类工作.在实验中,通过与其他多示例多标记算法对比,得出本文提出的算法取得了比其他分类算法更优的分类效果.
Abstract:Multi-Instance Multi-Label (MIML) learning is a novel machine learning framework in which an instance is described by multiple instances and associated with multiple labels. This framework has become a hot topic in the field of machine learning because of its excellent expressive ability for polysemous objects. The most direct way to solve the MIML classification problem is the degradation strategy, it takes the multiple instance learning or multiple label learning as a bridge, transforms the MIML problem into a series of binary classification problems. However, the correlation information among labels will be lost in the degradation process, which will affect the classification result. Based on these problems, this study proposes the MIMLSVM-LOC algorithm. The algorithm combines the improved MIMLSVM algorithm with a local label correlation method ML-LOC which considers the correlation information among labels in the training process. The algorithm first improves the K-medoids clustering algorithm in the MIMLSVM algorithm, and then uses the mixed Hausdorff distance to transform each instance packet into an instance, which degradate the MIML problem. Then, the ML-LOC algorithm is used to continue the classification work. In the experiment, the comparison experiment with other MIML algorithms, the result shows that the improved algorithm has better performance than other classification algorithms.
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基金项目:山东省自然科学基金(ZR2014FQ018)
引用文本:
李村合,田程程,姜宇.基于标记相关性的多示例多标记算法.计算机系统应用,2018,27(8):146-152
LI Cun-He,TIAN Cheng-Cheng,JIANG Yu.Multi-Instance Multi-Label Algorithm Based on Label Correlation.COMPUTER SYSTEMS APPLICATIONS,2018,27(8):146-152

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