# Graphs from Features: Tree-Based Graph Layout for Feature Analysis
## 2 Related Work
### Visual Feature Selection
Users can perform investigations by drilling down the data, restricting its range and pruning dimensions to examine different data levels. [PERSON] et al. (2018) proposed a projection-based visual analytics methodology to provide predictive feedback on classification systems design. Inside the tool, users can perform feature selection tasks, project items associated with the current selection, query feature's relevance related to labels (or groups of items), and map features using a chosen projection technique. Other approaches use regression models as a basis for generating feature space interactive exploration, or, in contrast, handle the visualization of features to support the generation and validation of regression models. [PERSON] and [PERSON] (2018) presented a framework for building regression models with visual support to show the relationships of features related to a selected target feature. Also, the framework allows investigating the relationships of features by disjoint partitions and pairs, together with feature ranks to assist the user. [PERSON] et al. (2018) presented the 3D Regression Heat Map, a regression analysis tool that shows all combinations of two or three independent features related to a target outcome. The visual interface works as a three-dimensional heat map, showing the results for subsequent user exploration. [PERSON] et al. (2018) apply logistic regression models to inspect the predictive power and to analyze potential subgroups of features. The approach defines three basic steps: feature selection according to univariate analysis indicators, evaluation of relationships of previously selected variables, and evaluation of regression models according to the selected subgroups. [PERSON] et al. (2018) proposed the RegressionExplorer, a tool that helps users finding and evaluating feature subsets and later applying them to regression models. An univariate analysis view presents single attribute significance indicators, which supports the search for robust feature subsets and the construction of logistic regression models. Graphs have been applied to represent objects and their relationships respectively as vertices and edges. Various works concern the display and interaction with graphs where vertices and edges have attributes that must be visualized together with the graph topology ([PERSON] et al., 2018). Other works represent features as vertices, and employ the graphs as a model that is explored visually. [PERSON] et al. (2018) built a visual approach based on a graph that tracks the data flow of time-varying multivariate data. Quantitatively, the model attempts to expose the information transfer between features using the transfer entropy concept of information transfer theory. The authors employ graphs to show the influence among all pairs of features in a user-chosen time step. The authors also show the approach's effectiveness to visualize information transfer for volumetric and particle datasets. [PERSON] et al. (2018) construct graphs where edges denote the strength of association between dimensions. Users can interactively choose vertices (features) to visit, and the framework computes the optimal route (best order), avoiding unrelated features, and then displays it in a corresponding parallel coordinates visualization. [PERSON] et al. (2018) proposed a framework that employs graphs to generate clusters of features. In those graphs each vertex represents a feature, and the edges encode the mutual information shared by features. The tool allows features inside clusters to be analyzed and selected by conditional entropy measures, and use parallel coordinates plots and isocontours to display data and spatial domains. To the best of our knowledge, previous literature have employed graphs to represent similarity between features mostly to support automatic methods for detecting feature relevance. Additionally, there have been quite a few frameworks to visually support feature analysis, but not with the properties proposed here.