A hierarchical nonlinear discriminant classifier trained through an evolutionary algorithm

This work builds on our earlier two papers where we developed method to train nonlinear discriminant classifier for 4-feature datasets. In this paper, the method has been formalized to include any number of features. A hierarchical nonlinear discriminant classifier builds models using a constrained pattern of feature combinations. The model is far more expressive than naïve Bayes, for example, which does not consider feature combinations at all; and the model is far more parsimonious and scalable than unconstrained genetic programming (for example), which does not rule out any feature combinations. The method can be used for knowledge acquisition and decision-making expert system as it can retrieve 100% accurate model from the dataset. The method can also be used for classification of unseen data. The method has been tested on popular test datasets present in the UCI repository. Two approaches are presented to apply a learned model to the test set. The first method consists of application of a single exact hierarchical model on the test set; another method is the application of a weighted sum of models present in each hierarchy. Results of this approach on the datasets studied here are found to be very competitive with the results in recent literature.