论文标题
在许多类别检测的许多课程中学习接受区域
Learning Acceptance Regions for Many Classes with Anomaly Detection
论文作者
论文摘要
可以通过学习所有类别的接受区域来获得的旨在确定观察属于的所有合理类别的新分类范式，旨在识别所有观测范围的新分类范式。许多现有的设定值分类方法没有考虑到训练数据中从未出现的新类别出现在测试数据中的可能性。此外，当类的数量较大时，它们在计算上的昂贵。我们提出了一种广泛的预测集（GPS）方法，以估计接受区域，同时考虑测试数据中新类的可能性。提出的分类器最小化预测集的预期大小，同时确保特定于类的精度至少为预先指定的值。与以前的方法不同，所提出的方法在准确性，效率和异常检测率之间达到了良好的平衡。此外，我们的方法可以与所有类平行应用以减轻计算负担。进行了理论分析和数值实验，以说明该方法的有效性。
Set-valued classification, a new classification paradigm that aims to identify all the plausible classes that an observation belongs to, can be obtained by learning the acceptance regions for all classes. Many existing set-valued classification methods do not consider the possibility that a new class that never appeared in the training data appears in the test data. Moreover, they are computationally expensive when the number of classes is large. We propose a Generalized Prediction Set (GPS) approach to estimate the acceptance regions while considering the possibility of a new class in the test data. The proposed classifier minimizes the expected size of the prediction set while guaranteeing that the class-specific accuracy is at least a pre-specified value. Unlike previous methods, the proposed method achieves a good balance between accuracy, efficiency, and anomaly detection rate. Moreover, our method can be applied in parallel to all the classes to alleviate the computational burden. Both theoretical analysis and numerical experiments are conducted to illustrate the effectiveness of the proposed method.
