An Ordinal Random Forest and Its Parallel Implementation with MapReduce

Ordinal decision tree (ODT) can effectively deal with monotonic classification problems. However, it is difficult for the existing ordinal decision tree algorithms to learning ODT from large data sets. Based on the variable consistency dominance based rough set approach (VC-DRSA), an ordinal random forest algorithm is proposed in this paper. Combining with the computing framework of MapReduce, the proposed ordinal random forest algorithm is paralleled on the platform of Hadoop, which improves the efficiency of the proposed algorithm. The feasibility and effectiveness of the proposed algorithm is verified by the experimental results.