Adaptive Semi-Supervised Random Forest Algorithm Based on Data Similarity

An adaptive semi-supervised stochastic forest algorithm based on data similarity is proposed. The coding and mapping of labeled and unlabeled data are carried out by using random forest. The pseudo-marking of unlabeled data is selected. Then the randomized forest is trained to improve the classification performance. The experimental results show that the proposed algorithm can effectively use the unmarked data to improve the classification accuracy.