论文标题
完全贝叶斯分析相关性矢量机分类的数据不平衡数据
Fully Bayesian Analysis of the Relevance Vector Machine Classification for Imbalanced Data
论文作者
论文摘要
相关矢量机（RVM）是一种基于贝叶斯稀疏模型从支持向量机（SVM）扩展的监督学习算法。与回归问题相比，很难进行RVM分类，因为没有封闭形式的解决方案的重量参数后部。原始RVM分类算法使用牛顿的方法进行优化，以获得权重参数后部的模式，然后通过Laplace方法中的高斯分布进行近似。它可以工作，但仅在贝叶斯框架中应用了频率方法。本文提出了一种通用贝叶斯分类的方法。我们猜想我们的算法与原始RVM分类算法的非融合估计值相比，获得了关注量的收敛估计。此外，提出了针对RVM分类的分层高位结构的完全贝叶斯方法，这改善了分类性能，尤其是在不平衡的数据问题中。通过数字研究，我们提出的算法获得了较高的分类精度率。完全贝叶斯分层超级优先级方法的表现优于不平衡数据分类的通用方法。
Relevance Vector Machine (RVM) is a supervised learning algorithm extended from Support Vector Machine (SVM) based on the Bayesian sparsity model. Compared with the regression problem, RVM classification is difficult to be conducted because there is no closed-form solution for the weight parameter posterior. Original RVM classification algorithm used Newton's method in optimization to obtain the mode of weight parameter posterior then approximated it by a Gaussian distribution in Laplace's method. It would work but just applied the frequency methods in a Bayesian framework. This paper proposes a Generic Bayesian approach for the RVM classification. We conjecture that our algorithm achieves convergent estimates of the quantities of interest compared with the nonconvergent estimates of the original RVM classification algorithm. Furthermore, a Fully Bayesian approach with the hierarchical hyperprior structure for RVM classification is proposed, which improves the classification performance, especially in the imbalanced data problem. By the numeric studies, our proposed algorithms obtain high classification accuracy rates. The Fully Bayesian hierarchical hyperprior method outperforms the Generic one for the imbalanced data classification.
