Pruning a Random Forest by Learning a Learning Algorithm
Ensemble Learning is a popular learning paradigm and finds its application in many diverse fields. Random Forest, a decision tree based ensemble learning algorithm has received constant attention in the research community due to its ability to learn complex rules and generalize well for unknown data. Identifying the number of base classifiers (trees) required for a particular dataset is one of the key questions addressed in this paper. Statistical analyses of individual base classifiers are carried out to prune the ensemble model without compromising the classification accuracy of the model. Learning the learned model, i.e. learning the statistics of the forest in its entirety along with the information available in the dataset can reveal the optimal thresholds that should be used to prune an ensemble model. Experimental results reveal that, on an average 78% of the trees were pruned on 26 different datasets obtained from the UCI repository. The impact of pruning was positive with 22 out of 26 datasets showing equal or better classification accuracy in comparison with the Classical Random Forest algorithm.
MLDM