Abstract

Random forest is well known as one of the best learning methods. In spite of its great success, it also has certain drawbacks: the heuristic learning rule does not effectively minimize the global training loss; the model size is usually too large for many real applications. To address the issues, we propose two techniques, global refifinement and global pruning, to improve a pre-trained random forest. The proposed global refifinement jointly relearns the leaf nodes of all trees under a global objective function so that the complementary information between multiple trees is well exploited. In this way, the fifitting power of the forest is signifificantly enhanced. The global pruning is developed to reduce the model size as well as the over-fifitting risk. The refifined model has better performance and smaller storage cost, as verifified in extensive experiments.