Parallel Ordinal Decision Tree And Decision Forest Based On MapReduce

Traditional ordinal decision tree(ODT) can effectively dea l with monotonic classification problems. However, it is very difficult for the existing ordinal decision tree algorithms to learning O DT from large data sets. In order to deal with the problem of generating an ODT from large datasets, this paper presents a parallel processing mechanism in the framework of Map Reduce. Similar to the traditional ordinal decision tree inductive algorithms, the rank mutual information(RMI) is still used to select the extended attributes. Differing from the calculation of RMI in the existing ordinal decision tree inductive algorithms, this paper applies a strategy of attribute parallelization to calculate the RMI. Experiments on large ordered data sets(which are generated artificially) confirm that our proposed algorithm is feasible. Experimental results show that our algorithm is effective and efficient from three aspects: speed- up, scale-up and size-up.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 Map Reduce, the proposed ordinal decision 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.