Ternary Bradley-Terry model-based decoding for multi-class classification and its extensions
Ternary Bradley-Terry model-based decoding for multi-class classification and its extensions
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DOI：
10.1007/s10994-011-5240-0
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发表时间：
2011-12
期刊：
影响因子：
7.5
通讯作者：
Takashi Takenouchi;S. Ishii
中科院分区：
文献类型：
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作者：
Takashi Takenouchi;S. Ishii
基于Bradley-Terry模型的多类分类器通过组合多个二进制分类器的输出来预测输入的多类标签，其中组合应优先设计为码字矩阵。码字矩阵最初设计为由+1和-1码组成，后来扩展为处理三进制码{+1，0，1，1}，即允许0码。这种扩展似乎有效，但实际上存在一个问题：二元分类器将代码为0的样本强制分类为+1或-1，但这种强制决策使得多类标签的预测变得模糊。在这篇文章中，我们提出了一个Boosting算法，通过允许一个“不关心”的类别对应于0代码处理三个类别，并提出了一个修改后的解码方法称为“三元”布拉德利-特里模型。此外，我们提出了一对夫妇的快速解码方案，减少了繁重的计算，由现有的布拉德利-特里模型为基础的解码。
A multi-class classifier based on the Bradley-Terry model predicts the multi-class label of an input by combining the outputs from multiple binary classifiers, where the combination should bea prioridesigned as a code word matrix. The code word matrix was originally designed to consist of +1 and −1 codes, and was later extended into deal with ternary code {+1,0,−1}, that is, allowing 0 codes. This extension has seemed to work effectively but, in fact, contains a problem: a binary classifier forcibly categorizes examples with 0 codes into either +1 or −1, but this forcible decision makes the prediction of the multi-class label obscure. In this article, we propose a Boosting algorithm that deals with three categories by allowing a ‘don’t care’ category corresponding to 0 codes, and present a modified decoding method called a ‘ternary’ Bradley-Terry model. In addition, we propose a couple of fast decoding schemes that reduce the heavy computation by the existing Bradley-Terry model-based decoding.