基于分类器链的多示例多标记算法  

Multi-instance multi-label algorithm based on classifier chain

作　　者：李村合[1] 田程程 董玉坤[1] LI Cun-he;TIAN Cheng-cheng;DONG Yu-kun(College of Computer and Communication Engineering,China University of Petroleum (East China),Qingdao 266580,China)

机构地区：[1]中国石油大学(华东)计算机与通信工程学院

出　　处：《计算机工程与设计》2019年第6期1580-1584,1625,共6页Computer Engineering and Design

基　　金：山东省自然科学基金项目(ZR2014FQ018);山东省优秀中青年科学家科研奖励基金项目(BS2015DX017)

摘　　要：退化方法是求解多示例多标记学习(MIML)问题常用的求解方式,但是在退化过程中会造成标记之间的关联信息丢失。对该问题进行研究,提出OCC-MIMLSVM+分类算法,将MIMLSVM+算法与有序分类器链(OCC)方法相结合,通过对分类器进行合理组织,将标记之间的关联信息融入至算法的训练过程中,解决信息丢失问题,提高分类准确率。实验结果表明,改进算法取得了比基准多示例多标记算法更好的分类效果。Degradation is a common solution to the problem of the MIML classification problem. However, the correlation information among labels may lost in the degradation process. Based on these problems, the OCC-MIMLSVM + algorithm was proposed. The MIMLSVM + algorithm was combined with the ordered classifier chain (OCC) method, the classifier was organized and the dependency relation between labels was integrated into the training process of the algorithm, so that the problem of information lost was solved, and the accuracy of classification was improved. Experimental results show that the improved algorithm achieves better classification results than the benchmark multi-instance multi-label algorithm.

关 键 词：多示例多标记学习 分类器链 标记依赖 信息丢失 支持向量机 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]