论文标题
使用乐观的分数比例强大的贝叶斯分类
Robust Bayesian Classification Using an Optimistic Score Ratio
论文作者
论文摘要
当关于课堂条件或上下文分布的信息有限时，我们使用乐观的分数构建贝叶斯上下文分类模型。乐观的分数搜索最合理的分布，可以解释属于上下文歧义集的所有分布中观察到的结果，该分布使用对平均值矢量的有限结构约束和基础上下文分布的协方差矩阵进行规定。我们表明，使用乐观分数比率的贝叶斯分类器在概念上具有吸引力，可提供坚实的统计保证，并且在计算上是可拖延的。我们在合成数据和经验数据上展示了所提出的乐观分数分类器的功能。
We build a Bayesian contextual classification model using an optimistic score ratio for robust binary classification when there is limited information on the class-conditional, or contextual, distribution. The optimistic score searches for the distribution that is most plausible to explain the observed outcomes in the testing sample among all distributions belonging to the contextual ambiguity set which is prescribed using a limited structural constraint on the mean vector and the covariance matrix of the underlying contextual distribution. We show that the Bayesian classifier using the optimistic score ratio is conceptually attractive, delivers solid statistical guarantees and is computationally tractable. We showcase the power of the proposed optimistic score ratio classifier on both synthetic and empirical data.
