Joint feature selection and hierarchical classifier design

This work presents a method for improving classifier accuracy through joint feature selection and hierarchical classifier design with genetic algorithms. The hierarchical classifier divides the classification problem into a set of smaller problems using multiple feature-selected classifiers in a tree configuration to separate the data into progressively smaller groups of classes. This allows the use of more specific feature sets for each set of classes. Several existing performance measures for evaluating the feature sets are investigated, and a new measure, count-based RELIEF is proposed. The joint feature selection and hierarchical classifier design method is tested on two artificial data sets. Results indicate that the feature selected hierarchical classifiers are able to achieve better accuracy than a non-hierarchical classifier using feature selection alone. The newly proposed performance measure is also tested and shown to provide a better indication of classifier performance than existing methods.