Novel Methods for the Feature Subset Ensemble Approach

Ensemble learning technique attracted much attention in the past few years. Instead of using a single prediction model, this approach utilizes a number of diverse accurate prediction models to do the job. Many methods have been proposed to build such accurate diverse ensembles, of which bagging and boosting were the most popular. Another method, called Feature Subset Ensembles (FSE), is thoroughly investigated in this work. This technique builds ensembles by assigning each individual prediction model in the ensemble a distinct feature subset from the pool of available features. In this paper several novel variations to the basic FSE are proposed. Extensive comparisons are carried out to compare the proposed FSE variants with the basic FSE approach.