A Taxonomy of Label Ranking Algorithms
A Taxonomy of Label Ranking Algorithms
复制标题
标签排名算法的分类
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
Liangliang Liu
中科院分区：
文献类型：
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作者：
Yangming Zhou;Yangguang Liu;Jiangang Yang;Xiaoqi He;Liangliang Liu
标签排序的学习问题越来越受到机器学习和数据挖掘界的关注。它的目标是在有限数量的标签上学习从实例到排名的映射。在本文中，我们致力于给国家的最先进的标签排名领域的概述，并提供了一个基本的分类的标签排名算法。具体来说，我们将这些标签排序算法分为四类，即分解方法，概率方法，基于相似性的方法，和其他方法。我们特别关注每个领域的最新进展。此外，我们还讨论了它们的优点和缺点，并强调了一些有待解决的有趣挑战。
The problem of learning label rankings is receiving increasing attention from machine learning and data mining community. Its goal is to learn a mapping from instances to rankings over a finite number of labels. In this paper, we devote to giving an overview of the state-of-the-art in the area of label ranking, and providing a basic taxonomy of the label ranking algorithms. Specifically, we classify these label ranking algorithms into four categories, namely decomposition methods, probabilistic methods, similarity-based methods, and other methods. We pay particular attention to the latest advances in each. Also, we discuss their strengths and weaknesses, and highlight some interesting challenges that remain to be solved.