论文标题
classSplom-一个散点图矩阵，可视化多类多维数据的分离
ClassSPLOM -- A Scatterplot Matrix to Visualize Separation of Multiclass Multidimensional Data
论文作者
论文摘要
在多维数据的多类分类中，用户希望建立一个类模型，以预测看不见的数据的标签。该模型对数据进行了培训，并在未见数据的数据上进行了测试，以评估其质量。结果可视化为混乱矩阵，该矩阵显示了正确预测了多少数据标签或与其他类混淆。数据的多维性质阻止了类的直接可视化，因此我们设计classSplom，以提供有关分类结果的更多感知见解。它使用Scatterplot矩阵（SPLOM）隐喻来可视化每对类别的数据的线性判别分析投影，以及一组接收操作曲线以评估其可信度。我们在阿拉伯语方言识别中说明了有关用例的classSplom。
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.
