Alexander G. Schwing
Alexander G. Schwing
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One problem in the field of statistics has been that everyone wants to be a theorist.
Part of this is envy - the real sciences are based on mathematical theory. In the universities for this century, the glamor and prestige has been in mathematical models and theorems, no matter how irrelevant.
(Leo Breiman)
Adaptive Random Forest - How many "experts" to ask before making a decision?
by: A.G. Schwing, C. Zach, Y. Zheng and M. Pollefeys
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.
For convenience we provide a general (non-adaptive) Random Forest C++ implementation:
  • Dependency: none
  • Tested on Windows (x64), Linux (x64) and Mac OS X (x64) operating systems.
  • See included README for further details regarding compilation.
  • The downloadable Random Forest algorithm is a non-adaptive implementation including an example that performs leave-one-out on the publicly available Ionosphere data set. We have other implementations (e.g., adaptive, parallelized) waiting for your real challenges. Please don't hesitate to contact us.
The provided implementation is licensed under GPL v3 (or higher). Upon request, other licensing options are available, e.g., if you want to use this implementation in a closed-source product.
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