Adaptively weighted large-margin angle-based classifiers
Adaptively weighted large-margin angle-based classifiers
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DOI：
10.1016/j.jmva.2018.03.004
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发表时间：
2018-07
影响因子：
1.6
通讯作者：
Sheng Fu;Sanguo Zhang;Yufeng Liu
中科院分区：
文献类型：
--
作者：
Sheng Fu;Sanguo Zhang;Yufeng Liu
大边距分类器是解决分类问题的有力技术。尽管人们对二元大边界分类器进行了大量的研究，但多类别分类问题更加复杂和具有挑战性。一种常见的方法是为具有和到零约束的k类问题构造k个不同的决策函数。然而，这样的约束可能是低效的。此外，许多大边际分类器对训练样本中的异常值很敏感。在本文中，我们使用基于角度的分类框架来避免显式的和至零约束，并提出了两种自适应加权大边界分类技术。我们的新方法在合适的条件下具有Fisher一致性和对异常值的鲁棒性。数值实验进一步表明，与现有方法相比，我们的方法具有竞争力和稳定性。
Large-margin classifiers are powerful techniques for classification problems. Although binary large-margin classifiers are heavily studied, multicategory problems are more complicated and challenging. A common approach is to construct k different decision functions for a k-class problem with a sum-to-zero constraint. However, such a constraint can be inefficient. Moreover, many large-margin classifiers can be sensitive to outliers in the training sample. In this article, we use the angle-based classification framework to avoid the explicit sum-to-zero constraint, and we propose two adaptively weighted large-margin classification techniques. Our new methods are Fisher consistent and more robust against outliers under suitable conditions. Numerical experiments further indicate that our methods give competitive and stable performance when compared with existing approaches.