论文标题
使用提升技术改进标签排名合奏
Improving Label Ranking Ensembles using Boosting Techniques
论文作者
论文摘要
标签排名是一个预测任务，涉及从有限集的实例和标签的排名（即订单）之间学习映射，这代表了它们与实例的相关性。提升是一种众所周知且可靠的集成技术，经常表现出比其他学习算法的表现。在为众多机器学习任务开发了增强算法时，标签排名任务被忽略了。在本文中，我们提出了一种专门为标签排名任务设计的增强算法。对24个半合成和现实标签排名数据集的拟议算法的广泛评估表明，它的表现明显胜过现有的最新标签排名算法。
Label ranking is a prediction task which deals with learning a mapping between an instance and a ranking (i.e., order) of labels from a finite set, representing their relevance to the instance. Boosting is a well-known and reliable ensemble technique that was shown to often outperform other learning algorithms. While boosting algorithms were developed for a multitude of machine learning tasks, label ranking tasks were overlooked. In this paper, we propose a boosting algorithm which was specifically designed for label ranking tasks. Extensive evaluation of the proposed algorithm on 24 semi-synthetic and real-world label ranking datasets shows that it significantly outperforms existing state-of-the-art label ranking algorithms.
