Scaled radial axes for interactive visual feature selection: A case study for analyzing chronic conditions

• Published in: Expert systems with applications 2018-06, Vol.100, p.182-196
• Main Authors: Sanchez, A., Soguero-Ruiz, C., Mora-Jiménez, I., Rivas-Flores, F.J., Lehmann, D.J., Rubio-Sánchez, M.
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Axes (reference lines); Chronic conditions; Chronic illnesses; Correlation analysis; Diabetes; Dimensional analysis; Exploratory data analysis; High-dimensional data visualization; Hypertension; Interactive feature selection; Machine learning; Medical chronic conditions; Scientific visualization; Visual analytics; Visualization; Web analytics
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2018.01.054

•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. In statistics, machine learning, and related fields, feature selection is the process of choosing a smaller subset of features to work with. This is an important topic since selecting a subset of features can help analysts to interpret models and data, and to decrease computational runtimes. While many techniques are purely automatic, the data visualization community has produced a number of interactive approaches where users can make decisions taking into account their domain knowledge. In this paper we propose a new visualization technique based on radial axes that allows analysts to perform feature selection effectively, in contrast to previous radial axes methods. This is achieved by employing alternative scaled axes that provide insight regarding the features that have a smaller contribution to the visualizations. Therefore, analysts can use the technique to carry out interactive backwards feature elimination, by discarding the least relevant features according to the information on the plots and their expertise. Our approach can be coupled with any linear dimensionality reduction method, and can be used when performing analyses of cluster structure, correlations, class separability, etc. Specifically, in this paper we focus on combining the proposed technique with methods designed for classification. Lastly, we illustrate the effectiveness of our proposal through a case study analyzing high-dimensional medical chronic conditions data. In particular, clinicians have used the technique for determining the most important features that discriminate between patients with diabetes and high blood pressure.