Parallelization of Online Random Forest

Context. Random Forests (RFs) is a very popular machine learning algorithm for mining large scale data. RFs is mainly known asan algorithm that operates in offline mode. However, in recent yearsimplementations of online random forests (ORFs) have been introduced. With multicore processors and successful implementation ofparallelism may result in increased performance of an algorithm, inrelation to its sequential implementation. Objectives. In this paper we develop and investigate the performanceof a parallel implementation of ORFs and compare the experimentalresults with its sequential counterpart. Methods. From using profiling tools on ORFs we located its bottlenecks and with this knowledge we used the implementation/experiment methodology to develop parallel online random forests (PORFs).Evaluation is done by comparing performance from ORFs and PORFs. Results. Experiments on common machine learning data sets showthat PORFs achieve equal classification to our execution of ORFs. However, there is a difference in classification on some data sets whencompared to results from another study. Furthermore, PORFs didn’tachieve any speed up compared to ORFs. In fact with the added overhead from pthreads PORFs takes longer time to finish than ORFs. Conclusions. We conclude that our parallelization of ORFs achievesequal classification performance as sequential ORFs. However, speedup wasn’t achieved with our chosen approach for parallelism. Possible solutions to achieve speed up is presented and suggested as futurework.