用于可视化分类结果的类别图
Class maps for visualizing classification results
AI总结：
本文提出类别图方法，通过可视化对象属于替代类别的概率、类内距离及离群程度，揭示分类结果中的标签偏差和潜在误标注，并在多种分类器与基准数据集上验证其有效性。
摘要：
分类是统计学和机器学习的重要工具。分类方法首先处理一组带有给定类别（标签）的训练对象，目标是在之后将新对象分配到这些类别之一。当在训练数据或测试数据上运行所得的预测方法时，可能出现对象被预测到与其给定标签不同的类别中的情况。这有时被称为标签偏差，并引发该对象是否被错误标注的问题。所提出的类别图反映了对象属于替代类别的概率、它与其给定类别中其他对象的距离，以及是否有一些对象远离所有类别。其目标是可视化分类结果的各个方面，以获得对数据的洞察。该显示方法针对判别分析、k近邻分类器、支持向量机、逻辑回归以及成对分类耦合进行了构建，并在多个基准数据集上进行了展示，其中包括一些关于图像和文本的数据集。
英文摘要：
Classification is a major tool of statistics and machine learning. A classification method first processes a training set of objects with given classes (labels), with the goal of afterward assigning new objects to one of these classes. When running the resulting prediction method on the training data or on test data, it can happen that an object is predicted to lie in a class that differs from its given label. This is sometimes called label bias, and raises the question whether the object was mislabeled. The proposed class map reflects the probability that an object belongs to an alternative class, how far it is from the other objects in its given class, and whether some objects lie far from all classes. The goal is to visualize aspects of the classification results to obtain insight in the data. The display is constructed for discriminant analysis, the k-nearest neighbor classifier, support vector machines, logistic regression, and coupling pairwise classifications. It is illustrated on several benchmark datasets, including some about images and texts.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
