An Optimization-based Framework to Learn Conditional Random Fields for Multi-label Classification.
An Optimization-based Framework to Learn Conditional Random Fields for Multi-label Classification.
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DOI：
10.1137/1.9781611973440.113
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
Hauskrecht M
中科院分区：
文献类型：
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作者：
Naeini MP;Batal I;Liu Z;Hong C;Hauskrecht M
本文研究了多标签分类问题，其中数据实例与多个，可能是高维的，标签向量。这个问题是特别具有挑战性的标签时，依赖和一个不能分解成一组独立的分类问题的问题。为了解决这个问题，并正确地表示标签依赖，我们提出并研究了成对条件随机场（CRF）模型。我们开发了一种新的方法来学习的结构和参数的CRF从数据。该方法最大化了观测标签的伪似然性，并依赖于快速邻近梯度下降来学习结构和有限记忆BFGS来学习模型的参数。多个数据集的实证结果表明，我们的方法优于多个多标签分类基线，包括最近发布的最先进方法。
This paper studies multi-label classification problem in which data instances are associated with multiple, possibly high-dimensional, label vectors. This problem is especially challenging when labels are dependent and one cannot decompose the problem into a set of independent classification problems. To address the problem and properly represent label dependencies we propose and study a pairwise conditional random Field (CRF) model. We develop a new approach for learning the structure and parameters of the CRF from data. The approach maximizes the pseudo likelihood of observed labels and relies on the fast proximal gradient descend for learning the structure and limited memory BFGS for learning the parameters of the model. Empirical results on several datasets show that our approach outperforms several multi-label classification baselines, including recently published state-of-the-art methods.