Read a little bit about ensembles and ensemble creation for class. [pdf]. The point behind ensembles is to create a number of classifiers, then evaluate an instance on all classifiers. The overall output of the ensemble is some function of the individual results, and is (hopefully) of a higher accuracy. The trick is creating the ensemble.

The paper describes a comparison of bagging (bootstrap aggregation) versus boosting and several random subspace / random tree / random forest variants.

The big point of the paper is that no one method is a sure-fire win versus bagging. Random-forests are significantly better sometimes, but have a significant loss (in the statistical sense). However, one key feature of the random-forest algorithms is that they are very fast, since they pick a random subset of features for evaluation. This can be a big win on data sets with large feature vectors. Genomic data, for example, has 65,000+ features -- it is computationally very slow to evaluate all features.

All that said, it is worth looking into building ensembles of classifiers to improve performance.