论文标题
通过识别培训数据的趋势来解决可视化推荐人的偏见：通过对情节社区提要的统计分析改善VIZML
Addressing Bias in Visualization Recommenders by Identifying Trends in Training Data: Improving VizML Through a Statistical Analysis of the Plotly Community Feed
论文作者
论文摘要
机器学习是一种有前途的可视化建议，因为它的高可扩展性和代表力。研究人员可以通过通过数据集和可视化示例训练输入数据来创建一个神经网络来预测输入数据的可视化。但是，这些机器学习模型可以反映其培训数据的趋势，可能会对他们的性能产生负面影响。我们的研究项目旨在通过通过统计分析来识别培训数据中的趋势来解决机器学习可视化建议系统中的训练偏见。
Machine learning is a promising approach to visualization recommendation due to its high scalability and representational power. Researchers can create a neural network to predict visualizations from input data by training it over a corpus of datasets and visualization examples. However, these machine learning models can reflect trends in their training data that may negatively affect their performance. Our research project aims to address training bias in machine learning visualization recommendation systems by identifying trends in the training data through statistical analysis.
