A comparison of methods for multiclass support vector machines
A comparison of methods for multiclass support vector machines
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DOI：
10.1109/72.991427
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发表时间：
2002-03-01
影响因子：
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通讯作者：
Lin, CJ
中科院分区：
文献类型：
--
作者：
Hsu, CW;Lin, CJ
支持向量机（SVM）最初是为二进制分类而设计的。如何有效地将其扩展到多类分类仍然是一个正在进行的研究问题。已经提出了几种方法，其中通常我们通过组合几个二进制分类器来构建多类分类器。一些作者还提出了同时考虑所有类的方法。由于它是计算更昂贵的解决多类问题，比较这些方法使用大规模的问题还没有认真进行。特别是对于一步解决多类SVM的方法，需要更大的优化问题，所以到目前为止，实验仅限于小数据集。在本文中，我们给出了两个这样的“所有在一起”的方法分解实现。然后，我们比较他们的性能与三种方法的基础上二进制分类：“一对所有”，“一对一”，有向无环图SVM（DAGSVM）。实验结果表明，“一对一”和DAG方法比其他方法更适合于实际应用。结果还表明，对于大型问题，一次考虑所有数据的方法通常需要较少的支持向量。
Support vector machines (SVMs) were originally designed for binary classification. How to effectively extend it for multiclass classification is still an ongoing research issue. Several methods have been proposed where typically we construct a multiclass classifier by combining several binary classifiers. Some authors also proposed methods that consider all classes at once. As it is computationally more expensive to solve multiclass problems, comparisons of these methods using large-scale problems have not been seriously conducted. Especially for methods solving multiclass SVM in one step, a much larger optimization problem is required so up to now experiments are limited to small data sets. In this paper we give decomposition implementations for two such "all-together" methods. We then compare their performance with three methods based on binary classifications: "one-against-all," "one-against-one," and directed acyclic graph SVM (DAGSVM). Our experiments indicate that the "one-against-one" and DAG methods are more suitable for practical use than the other methods. Results also show that for large problems methods by considering all data at once in general need fewer support vectors.