An Approach towards Optimizing Random Forest using Dynamic Programming Algorithm:

In this paper, we show that the problem of selection of optimal subset of Random Forest follows the dynamic programming paradigm. Applying this approach to various UCI data-sets, corresponding subsets are obtained and studied. Analysis of the subsets reveal that optimal subsets do exist and that they are not unique. Moreover the size of these subsets is small fraction of the original RF (around 1/10th) and that accuracy of these subsets is a discrete valued function over its range.

Heuristic Based Improvements for Effective Random Forest Classifier:

The paper describes the experiments done to study the performance Random Forest Algorithm by applying various heuristics.The paper goes on to analyze the heuristics and their combined effect towards improving the performance of the algorithm.