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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. |
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| (Leo Breiman) |
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Adaptive Random Forest - How many "experts" to ask before making a decision?
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| by: A.G. Schwing, C. Zach, Y. Zheng and M. Pollefeys
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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.
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Preprint: 
Supplementary: 
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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.
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License:
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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Downloads:
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