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计算机系统应用:2019,28(4):131-138
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基于全局和局部标签相关性的MIMLSVM改进算法
(中国石油大学(华东)计算机与通信工程学院, 青岛 266580)
Improved MIMLSVM Algorithm Based on Global and Local Label Correlations
(School of Computer & Communication Engineering, China University of Petroleum, Qingdao 266580, China)
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投稿时间:2018-10-10    修订日期:2018-10-30
中文摘要: 多示例多标记学习是用多个示例来表示一个对象,同时该对象与多个类别标记相关联的新型机器学习框架.设计多示例多标记算法的一种方法是使用退化策略将其转化为多示例学习或者是多标记学习,最后退化为传统监督学习,然后使用某种算法进行训练和建模,但是在退化过程中会有信息丢失,从而影响到分类准确率.MIMLSVM算法是以多标记学习为桥梁,将多示例多标记学习问题退化为传统监督学习问题求解,但是该算法在退化过程中没有考虑标记之间的相关信息,本文利用一种既考虑到全局相关性又考虑到局部相关性的多标记算法GLOCAL来对MIMLSVM进行改进,实验结果显示,改进的算法取得了良好的分类效果.
中文关键词: 多示例多标记  局部性  全局性  退化  MIMLSVM  GLOCAL
Abstract:Multi-Instance Multi-Label (MIML) learning is new machine learning framework where an example is described by multiple instances and associated with multiple classes of labels. A method of designing MIML algorithm is to identify its equivalence in the traditional supervised learning framework, using multi-instance learning or multi-label learning as the bridge, then using an algorithm for training and modeling. But in the degradation process, there will be the loss of information, then affecting the classification accuracy. This study improves the MIMLSVM algorithm by using a multi-label algorithm GLOCAL that considers both global and local correlations. The experimental results show that the improved algorithm has achieved sound classification results.
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基金项目:山东省自然科学基金项目(ZR2014FQ018)
引用文本:
李村合,张振凯.基于全局和局部标签相关性的MIMLSVM改进算法.计算机系统应用,2019,28(4):131-138
LI Cun-He,ZHANG Zhen-Kai.Improved MIMLSVM Algorithm Based on Global and Local Label Correlations.COMPUTER SYSTEMS APPLICATIONS,2019,28(4):131-138

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