Boosting Kernel Discriminant Analysis with Adaptive Kernel Selection
Boosting Kernel Discriminant Analysis with Adaptive Kernel Selection
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DOI：
10.1007/3-211-27389-1_103
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发表时间：
2005
期刊：
影响因子：
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通讯作者：
S. Kita;S. Maekawa;S. Ozawa;S. Abe
中科院分区：
文献类型：
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作者：
S. Kita;S. Maekawa;S. Ozawa;S. Abe
在本文中，我们提出了一种新的方法来提高分类性能的基础上Boosting通过引入非线性鉴别分析的特征选择。为了减少假设之间的依赖性，每个假设被构造在由核判别分析（KDA）形成的不同的特征空间中。然后，基于AdaBoost对这些假设进行整合。为了在实际时间内对每次Boosting迭代进行KDA，还提出了一种新的核选择方法。对血细胞数据和甲状腺数据进行了实验，以评估所提出的方法。实验结果表明，该方法在不需要进行耗时的参数搜索的情况下，几乎达到了支持向量机的最佳性能。
In this paper, we present a new method to enhance classification performance based on Boosting by introducing nonlinear discriminant analysis as feature selection. To reduce the dependency between hypotheses, each hypothesis is constructed in a different feature space formed by Kernel Discriminant Analysis (KDA). Then, these hypotheses are integrated based on AdaBoost. To conduct KDA in each Boosting iteration within realistic time, a new method of kernel selection is also proposed. Several experiments are carried out for the blood cell data and thyroid data to evaluate the proposed method. The result shows that it is almost the same as the best performance of Support Vector Machine without any time-consuming parameter search.