Learning and Inference in Probabilistic Classifier Chains with Beam Search
Learning and Inference in Probabilistic Classifier Chains with Beam Search
复制标题
使用波束搜索在概率分类器链中学习和推理
DOI：
10.1007/978-3-642-33460-3_48
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发表时间：
2012
期刊：
影响因子：
7.5
通讯作者：
C. Elkan
中科院分区：
文献类型：
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作者：
Abhishek Kumar;Shankar Vembu;A. Menon;C. Elkan
多标签学习是二分类的扩展，具有挑战性和实用性。最近，一种被称为概率分类器链（PCCs）的多标签学习方法被提出，它具有许多吸引人的特性，如概念简单、灵活性和理论合理性。然而，PCCs面临着标签数量呈指数级增长的计算问题，以及在训练时对标签的适当排序敏感的实际问题。在本文中，我们展示了如何使用经典的波束搜索技术来解决这两个问题。具体来说，我们展示了如何使用束搜索来执行易于处理的测试时间推断，以及如何将束搜索与训练相结合以确定合适的标签排序。在一系列多标签数据集上的实验结果表明，这些提议的变化极大地扩展了PCCs的实际可行性。
Multilabel learning is an extension of binary classification that is both challenging and practically important. Recently, a method for multilabel learning called probabilistic classifier chains (PCCs) was proposed with numerous appealing properties, such as conceptual simplicity, flexibility, and theoretical justification. However, PCCs suffer from the computational issue of having inference that is exponential in the number of tags, and the practical issue of being sensitive to the suitable ordering of the tags while training. In this paper, we show how the classical technique of beam search may be used to solve both these problems. Specifically, we show how to use beam search to perform tractable test time inference, and how to integrate beam search with training to determine a suitable tag ordering. Experimental results on a range of multilabel datasets show that these proposed changes dramatically extend the practical viability of PCCs.
DOI：
--
发表时间：
2012-06
期刊：
Proceedings of the ... International Conference on Machine Learning. International Conference on Machine Learning
影响因子：
--
作者：
A. Menon;Xiaoqian Jiang;Shankar Vembu;C. Elkan;L. Ohno-Machado
通讯作者：
A. Menon;Xiaoqian Jiang;Shankar Vembu;C. Elkan;L. Ohno-Machado