基于有序划分的增强支持向量机文本分类

Yong Shi, Peijia Li, Lingfeng Niu
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引用次数: 0

摘要

序数回归在过去几年中受到越来越多的关注。它的目的是按顺序对模式进行分类。随着数据的爆炸式增长,被称为SVMOP的有序划分支持向量机方法因其处理大规模数据的便捷性而凸显出其优势。然而,用于有序回归的SVMOP方法并没有得到太多的应用。正如我们所知,处理错标样本的成本应该是不同的,如何使用它们在模型构建中起着主导作用。而L2-loss会增大代价敏感性,目前还没有应用到支持向量机的有序划分中。本文提出了带L2-loss的SVMOP有序回归方法。数值结果表明,该方法优于具有L1-loss的SVMOP方法和其他正常回归模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Augmented SVM with ordinal partitioning for text classification
Ordinal regression has received increasing interest in the past years. It aims to classify patterns by an ordinal scale. With the the explosive growth of data, the method of SVM with ordinal partitioning called SVMOP highlights its advantages due to its convenience of dealing with large scale data. However, the method of SVMOP for ordinal regression has not been exploited much. As we know, the costs should be different when dealing with mislabeled samples and how to use them plays a dominant role in model building. However, L2-loss which could enlarge the cost sensitivity has not been applied into SVM ordinal partition yet. In this paper, we propose the method of SVMOP with L2-loss for ordinal regression. Numerical results show that our approach outperforms the method of SVMOP with L1-loss and other ordianl regression models.
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