A Score Fusion Method Using a Mixture Copula
A Score Fusion Method Using a Mixture Copula
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一种使用混合Copula的分数融合方法
DOI：
10.1007/978-3-319-44406-2_16
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Jun Miyazaki
中科院分区：
文献类型：
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作者：
Takuya Komatsuda ; Atsushi Keyaki ; Jun Miyazaki
在本文中，我们提出了一个分数融合方法，使用混合Copula，可以考虑复杂的依赖关系之间的多个相关性分数，以提高信息检索的有效性。多个相关性分数的组合已被证明与单个分数相比是有效的。常用的分数融合方法有线性组合法和学习排序法。线性组合不能捕捉多个分数的非线性依赖性。学习排名产生的输出使得理解模型变得困难。这些问题可以通过使用Copula来解决，Copula是一种统计框架，因为它可以捕获非线性依赖关系，并为模型提供可解释的原因。虽然一些研究应用Copula来评分融合并证明了其有效性，但他们的方法采用单峰Copula，因此难以捕获复杂的依赖关系。因此，我们引入了一种新的分数融合方法，使用混合copula来处理复杂的依赖关系的分数，然后，我们评估我们提出的方法的准确性。在大规模文档集CocketWeb '09上的实验表明，在某些情况下，我们提出的方法明显优于线性组合和其他现有的方法，使用单峰Copula。
In this paper, we propose a score fusion method using a mixture copula that can consider complex dependencies between multiple relevance scores in order to improve the effectiveness of information retrieval. The combination of multiple relevance scores has been shown to be effective in comparison with a single score. Widely used score fusion methods are linear combination and learning to rank. Linear combination cannot capture the non-linear dependency of multiple scores. Learning to rank yields output that makes it difficult to understand the models. These problems can be solved by using a copula, which is a statistical framework, because it can capture the non-linear dependency and also provide an interpretable reason for the model. Although some studies apply copulas to score fusion and demonstrate the effectiveness, their methods employ a unimodal copula, thus making it difficult to capture complex dependencies. Therefore, we introduce a new score fusion method that uses a mixture copula to handle the complicated dependencies of scores; then, we evaluate the accuracy of our proposed method. Experiments onClueWeb’09, a large-scale document set, show that in some cases, our proposed method significantly outperforms linear combination and others existing methods that use a unimodal copula.