Boosting the fisher linear discriminant with random feature subsets

Boosting increases the recognition accuracy of many types of classifiers. However, studies show that for the Fisher Linear Discriminant (FLD), a simple and widely used classifier, boosting does not lead to a significant increase in accuracy. In this paper, a new method for adapting the FLD into the boosting framework is proposed. This method, the AdaBoost-RandomFeatureSubset-FLD (AB-RFS-FLD), uses a different, randomly chosen subset of features for learning in each boosting round. The new method achieves significantly better accuracy than both single FLD and FLD with boosting, with improvements reaching 6% in some cases. We show that the good performance can be attributed to higher diversity of the individual FLDs, as well as to the better generalization abilities.