论文标题
在部分拒绝选项下的分类
Classification Under Partial Reject Options
论文作者
论文摘要
我们研究了贝叶斯模型的设定值分类，其中数据源自有限数量的可能的假设之一。因此，我们考虑了分类集类别的大小范围从0到$ n $的情况。空套对应于一个异常值，尺寸1代表了一个坚定的决定，该决定列出了一个假设，尺寸$ n $对应于对分类的拒绝，而尺寸$ 2 \ ldots，n-1 $表示部分拒绝，其中一些假设被排除在进一步分析之外。我们通过设定的参数引入了奖励功能的一般框架，并得出相应的最佳贝叶斯分类器，用于假设的均匀块以及将假设分配到块内和块之间的模棱两可的块时，范围内的歧义是不同的严重性。我们使用鸟类学数据集说明了分类，分类单元分为使用MCMC估计的块和参数。相关的奖励函数的调整参数是通过交叉验证选择的。
We study set-valued classification for a Bayesian model where data originates from one of a finite number $N$ of possible hypotheses. Thus we consider the scenario where the size of the classified set of categories ranges from 0 to $N$. Empty sets corresponds to an outlier, size 1 represents a firm decision that singles out one hypotheses, size $N$ corresponds to a rejection to classify, whereas sizes $2\ldots,N-1$ represent a partial rejection, where some hypotheses are excluded from further analysis. We introduce a general framework of reward functions with a set-valued argument and derive the corresponding optimal Bayes classifiers, for a homogeneous block of hypotheses and for when hypotheses are partitioned into blocks, where ambiguity within and between blocks are of different severity. We illustrate classification using an ornithological dataset, with taxa partitioned into blocks and parameters estimated using MCMC. The associated reward function's tuning parameters are chosen through cross-validation.
