用于多标记学习的分类器圈方法  

Classifier Circle Method for Multi-Label Learning

作　　者：王少博[1] 李宇峰[1] 

机构地区：[1]计算机软件新技术国家重点实验室(南京大学),江苏南京210023

出　　处：《软件学报》2015年第11期2811-2819,共9页Journal of Software

基　　金：国家自然科学基金(61403186);国家高技术研究发展计划(863)(2015AA015406);江苏省基础研究计划(自然科学基金)(BK20140613)

摘　　要：如何利用标记间关系来提高学习性能,是多标记学习领域的一个重要问题.分类器链方法及其变型是解决这类问题的一个有效技术.然而,它的学习过程需要预先给定标记的学习次序,这个信息真实情况难以获得.次序选择不当会导致学习性能提高受限.针对这个问题,提出用于多标记学习的分类器圈方法.该方法随机生成标记的学习次序,通过圈结构依次迭代地更新每个标记的分类器.实验结果表明,该方法在多个数据集上取得了比分类器链方法以及一系列经典多标记学习方法更好的性能.Exploiting label relationship to help improve learning performance is important for multi-label learning. The classifier chain method and its variants are shown to be a powerful solution to such a problem. However, its learning process requires the ordering of labels, which is hard to obtain in real-world situations, and incorrect label ordering may cause a suboptimal performance. To overcome the drawback, this paper presents a classifier circle method for multi-label learning. It initializes the label ordering randomly, and then subsequently and iteratively updates the classifier for each label by connecting the labels as a circle. Experimental results on a number of data sets show that the proposal outperforms classifier chains method as well as many state-of-the-art multi-label methods.

关 键 词：多标记学习 标记关系 分类器圈 分类器链 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]