Loan Default Prediction on Large Imbalanced Data Using Random Forests
Lifeng Zhou, Hong Wang · TELKOMNIKA Indonesian Journal of Electrical Engineering · 2012
In this paper, we propose an improved random forest algorithm which allocates weights to decision trees in the forest during tree aggregation for prediction and their weights are easily calculated based on out-of-bag errors in training. We compare the performance of our proposed algorithm and the original one on loan default prediction datasets . We also use these two algorithms to create two kinds of balanced random forests to deal with imbalanced data problem. Experiments results show that our proposed algorithm beat s the original r andom f orest in terms of both balanced and overall accuracy metric s . Experiments also show that parallel r andom f orests can greatly improve r andom forests’ efficiency during the learning process. DOI: http://dx.doi.org/10.11591/telkomnika.v10i6.1323