Using Local Rules in Random Forests of Decision Trees

We propose to use local labeling rules in random forests of decision trees for effectively classifying data. The decision rules use the majority vote for labeling at terminal-nodes in decision trees, maybe making the classical random forest algorithm degrade the classification performance. Our investigation aims at replacing the majority rules with the local ones, i.e. support vector machines to improve the prediction correctness of decision forests. The numerical test results on 8 datasets from UCI repository and 2 benchmarks of handwritten letters recognition showed that our proposal is more accurate than the classical random forest algorithm.