Improved Random Forest for Classification

We propose an improved random forest classifier that performs classification with a minimum number of trees. The proposed method iteratively removes some unimportant features. Based on the number of important and unimportant features, we formulate a novel theoretical upper limit on the number of trees to be added to the forest to ensure improvement in classification accuracy. Our algorithm converges with a reduced but important set of features. We prove that further addition of trees or further reduction of features does not improve classification performance. The efficacy of the proposed approach is demonstrated through experiments on benchmark data sets. We further use the proposed classifier to detect mitotic nuclei in the histopathological data sets of breast tissues. We also apply our method on the industrial data set of dual-phase steel microstructures to classify different phases. Results of our method on different data sets show significant reduction in an average classification error compared with a number of competing methods.