# Graphs from Features: Tree-Based Graph Layout for Feature Analysis
## 5 Results
### Selecting Features of Interest by Relevance  
One of the most common tasks in feature selection is to define a small set of features that properly describes the data. That usually reflects in better segregation of labels when plotting projections of the data based on selected features, as exemplified above. In the following text we present three cases of feature selection for practical applications that are being highly benefited by this framework. They are meant to reflect the effectiveness of GFF for different sources of data and for different numbers of points and attributes. Figure 4: Soundscape data from two forest areas in Costa Rica. (**a**) Initial similarity minimum spanning tree (MST). (**b**) Added edges in increasing order of similarity between nodes (features). (**c**) Further edges added to (**b**). Three groups of features can still be observed. (**d**) Force-based layout identifying three main groups of features. (**e**) With further edge addition still two separate groups of features remain. (**f**) Same tree from (**a**) presented as sunburst. (**g**) Addition of low similarity edges employing the edge histogram. (**h**) Preliminary feature selection from (**a,f,g**). (**i**) Resulting visualization of soundscapes after a projection with the selected features. **Case 1: Feature selection to find discriminant features for soundscape ecology data. **  
We have analyzed several datasets from the soundscape ecology application for the purpose of finding relationship between extracted features and particular environmental descriptions. The soundscape recordings used in these tests are part of a long term ecological research project within the Cantareira-Mantiqueira corridor, a cooperation of natural parks in the State of Sao Paulo, Brazil. The audio data were recorded within 22 landscapes distributed in the region. The dataset has more than 40,000 sound files of one minute each. In the current case, a total of 1662 sound files were used. They were labeled by experts, who tagged 822 recordings for birds and 840 for insects. In a previous work [52], a method was developed to identify the most discriminant features for categorizing sound events in soundscapes. The goal for this dataset is to find features that are capable of distinguishing two categories of sound events (bird and insect) in the soundscape. A set of 238 features were extracted from the dataset, as Temporal Entropy (Ht), Spectral Entropy (Hs), Acoustic Entropy (H), Acoustic Complexity Index (ACI), Shannon Index (H'), Mel Frequency Cepstral Coefficients (MFCCs) and some of Gray level co-concurrence matrix (GLCM). During the exploration of the feature set with the implemented framework, we have interacted with the structure and layout of the tree and graph and with the relevance bar. The relevance bar highlights features by correlation between them and the target label (Figure 5a-h). The combination these tools allowed us to discover some sets of highly discriminating features having 10 (Figure 5a) and 31 features (Figure 5b), evidencing a good degree of segregation between sounds of birds and insects. In the figures, larger vertices with a darker color indicate the most discriminating features. The images in Figure 5i-l show the LSP projections of the instances in the dataset under study for each selected feature set. We can see that the values of silhouette coefficient \(S\) decrease when less relevant features are added. An exploratory analysis of the features allows us to gain knowledge of their behaviour in relation to the task, in this case segregation. The tree and graph layouts of the feature set afford interpretation of sets of features that may be important to the task at hand. In Figure 5a-d, a distinguished group of features can be easily located in a clustered branch to the right of the layout. A closer inspection revealed non-informative and noisy attributes that ended up plotted together and that were eventually discarded from the dataset, thus reducing features from 238 to 141.