Implementing Multi-class Classifiers by One-class Classification Methods
Implementing Multi-class Classifiers by One-class Classification Methods
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DOI：
10.1109/ijcnn.2006.246699
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发表时间：
2006-10
期刊：
影响因子：
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通讯作者：
Tao Ban;S. Abe
中科院分区：
文献类型：
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作者：
Tao Ban;S. Abe
本文讨论了如何用一类分类器集成来实现多类分类器的问题。首先为每一类训练一类分类器，然后基于最小距离规则构造决策函数。研究了两种单类分类器：支持向量域描述方法和基于核主成分分析的方法。这两种方法都能在特征空间中工作，并能处理非线性分类问题。在一些基准数据集上的实验表明，经过精心调整的参数所提出的方法在具有与支持向量机相当的泛化能力的同时，还具有其他一些优点。
In this paper we address the problem of how to implement a multi-class classifier by an ensemble of one-class classifiers. One-class classifiers are first trained for each class and then a decision function is formulated based on minimum distance rules. Two kinds of one-class classifiers are explored: the support vector domain description and a kernel principle component analysis based method. Both of the two methods can work in the feature space and deal with nonlinear classification problems. Experiments on some benchmark datasets show that the proposed methods with carefully tuned parameters have comparable generalization ability with support vector machines while having some other advantages.