Figure 11.

Feature importance. Sequential forward selection accuracy converged to one after the addition of features 1, 2, and 4 using both the neural network and naïve Bayes classifiers. From the random forest algorithm, the effect on classification error was increased most by the random permutation of features 1, 2, 3, and 4. Features 1: CoM x-coordinate, 2: CoM y-coordinate, and 4: ellipse fit orientation were found to be the most important features and using only these three features in combination with the neural network and naïve Bayes classifiers enabled perfect classification.