Scaled radial axes for interactive visual feature selection: A case study for analyzing chronic conditions
作者：
Highlights：
• We propose a new radial axes method to help visual backward feature selection.
• Experts can incorporate domain knowledge to analyze classes through LMNN, NCA, etc.
• The method reduces clutter in visualization compared to other radial axes plots.
• We conducted different experiments with several public data sets.
• We present a case study using high dimensional data of chronic medical conditions.
摘要
•We propose a new radial axes method to help visual backward feature selection.•Experts can incorporate domain knowledge to analyze classes through LMNN, NCA, etc.•The method reduces clutter in visualization compared to other radial axes plots.•We conducted different experiments with several public data sets.•We present a case study using high dimensional data of chronic medical conditions.
论文关键词：High-dimensional data visualization,Interactive feature selection,Visual analytics,Exploratory data analysis,Medical chronic conditions
论文评审过程：Received 30 October 2017, Revised 6 January 2018, Accepted 27 January 2018, Available online 6 February 2018, Version of Record 16 February 2018.
论文官网地址：https://doi.org/10.1016/j.eswa.2018.01.054
