2022/03/09 by Allen Tu, Tu, Allen, Priyanka Mehta +8
Computer Science · #Data Analysis with R #Data Visualization and Analytics #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #cs.HC #cs.IR #cs.LG
paper · pdf · doi:10.48550/arxiv.2203.04937
arxiv created 2022/03/09 · openalex publication_date 2022/03/09 · arxiv updated 2022/03/10 · openalex created_date 2022/05/05 · openalex updated_date 2026/07/28
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.