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Random Forest (RF) is an ensemble classification technique that was developedby Breiman over a decade ago. Compared with other ensemble techniques, it hasproved its accuracy and superiority. Many researchers, however, believe thatthere is still room for enhancing and improving its performance in terms ofpredictive accuracy. This explains why, over the past decade, there have beenmany extensions of RF where each extension employed a variety of techniques andstrategies to improve certain aspect(s) of RF. Since it has been provenempirically that ensembles tend to yield better results when there is asignificant diversity among the constituent models, the objective of this paperis twofolds. First, it investigates how an unsupervised learning technique,namely, Local Outlier Factor (LOF) can be used to identify diverse trees in theRF. Second, trees with the highest LOF scores are then used to produce anextension of RF termed LOFB-DRF that is much smaller in size than RF, and yetperforms at least as good as RF, but mostly exhibits higher performance interms of accuracy. The latter refers to a known technique called ensemblepruning. Experimental results on 10 real datasets prove the superiority of ourproposed extension over the traditional RF. Unprecedented pruning levelsreaching 99% have been achieved at the time of boosting the predictive accuracyof the ensemble. The notably high pruning level makes the technique a goodcandidate for real-time applications.

An Outlier Detection-based Tree Selection Approach to Extreme Pruning of Random Forests