Adaptive Random Forest - How many ``experts'' to ask before making a decision?

How many people should you ask if you are not sure about your way? We provide an answer to this question for Random Forest classification. The presented method is based on the statistical formulation of confidence intervals and conjugate priors for binomial as well as multinomial distributions. We derive appealing decision rules to speed up the classification process by leveraging the fact that many samples can be clearly mapped to classes. Results on test data are provided, and we highlight the applicability of our method to a wide range of problems. The approach introduces only one non-heuristic parameter, that allows to trade-off accuracy and speed without any re-training of the classifier. The proposed method automatically adapts to the difficulty of the test data and makes classification significantly faster without deteriorating the accuracy.

• Type: Journal
• Year: 2011
• Where: CVPR
• Authors: Alexander Schwing, Christopher Zach, Yefeng Zheng, Marc Pollefeys
• Keywords: Computer Vision, CVPR 2011, Multinomial Distributions, Random Forest Classification, Test Data