Sequential Boosting for Learning a Random Forest Classifier

This paper introduces a novel tree induction algorithm called sequential Random Forest (sRF) to improve the detection accuracy of a standard Random Forest classifier. Observations have shown that the overall performance of a forest is strongly influenced by the number of training samples. The main idea is to sequentially adapt the number of training samples per class so that each tree better complements the existing trees in the whole forest. Further, we propose a weighted majority voting with respect to a class and tree specific error rate for decreasing the influence of poorly performing trees. The sRF algorithm shows competing results in comparison to state-of-the-art approaches using two datasets for object recognition, two standard machine learning datasets and three datasets for human action recognition.