ClassSPLOM--一种多类多维数据可视化分离的散点图矩阵
ClassSPLOM -- A Scatterplot Matrix to Visualize Separation of Multiclass Multidimensional Data
摘要：
在多维数据的多类分类中，用户希望建立类的模型来预测未见过数据的标签。该模型在数据上进行训练，并在具有已知标签的未知数据上进行测试，以评估其质量。结果被可视化为混淆矩阵，显示有多少数据标签被正确预测或与其他类别混淆。数据的多维性质阻止了类的直接可视化，因此我们设计了ClassSPLOM，以提供有关分类结果的更多感知见解。它使用散点图矩阵（SPLOM）隐喻来可视化每对类的数据的线性判别分析投影和一组接收操作曲线来评估其可信度。我们说明ClassSPLOM在阿拉伯语方言识别的用例。
英文摘要：
In multiclass classification of multidimensional data, the user wants to build a model of the classes to predict the label of unseen data. The model is trained on the data and tested on unseen data with known labels to evaluate its quality. The results are visualized as a confusion matrix which shows how many data labels have been predicted correctly or confused with other classes. The multidimensional nature of the data prevents the direct visualization of the classes so we design ClassSPLOM to give more perceptual insights about the classification results. It uses the Scatterplot Matrix (SPLOM) metaphor to visualize a Linear Discriminant Analysis projection of the data for each pair of classes and a set of Receiving Operating Curves to evaluate their trustworthiness. We illustrate ClassSPLOM on a use case in Arabic dialects identification.
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。
