Figure 3. Classification accuracy plot providing an overview of the individual (grey bars) and cumulative (red line and circles) contribution of each feature (in the in which they were selected in the stepwise forward selection (SFS) process).
2.5 Feature visualization
Above, under “Feature selection” we already mentioned the three objectives with feature selection: improving interpretability, reducing overfitting, reducing computational requirements. Visualisation of the features can further assist in deciding on the features to use in the ultimate behaviour classification model, yet its main use is in deciding if any behaviour types should be combined to ultimately improve behaviour classification performance. Alternatively, the visualisation may also lead to considering splitting up existing behaviour types into multiple behaviours. In other words, this visualisation aids in evaluating the behaviour set.
The rabc package offers three ways to visualize features. The first two visualise the features in isolation whereas the third is an integrative approach where entire feature domains are analysed collectively. The first of the visualisation methods, plot_feature, draws individual values of features ordered by behaviour (Fig. 4). The second, plot_grouped_feature, produces a boxplot of a selected feature for all behaviour types, as demonstrated for the ODBA feature in Fig. 5. In the case of the White Stork dataset it suggests clear differentiation of behaviours by ODBA with a trend of ODBA decreasing going from active flight via walking to passive flight, standing and sitting. The third and most important, integrative approach uses Uniform Manifold Approximation and Projection (UMAP).