An Extended Study of the Discriminant Random Forest.

Classification technologies have become increasingly vital to information analysis systems that rely upon collected data to make predictions or informed decisions. Many approaches have been developed, but one of the most successful in recent times is the random forest. The discriminant random forest is a novel extension of the random forest classification methodology that leverages linear discriminant analysis to perform multivariate node splitting during tree construction. An extended study of the discriminant random forest is presented which shows that its individual classifiers are stronger and more diverse than their random forest counterparts, yielding statistically significant reductions in classification error of up to 79.5%. Moreover, empirical tests suggest that this approach is computationally less costly with respect to both memory and efficiency. Further enhancements of the methodology are investigated that exhibit significant performance improvements and greater stability at low false alarm rates.