Parameter Tuning using the Out-of-Bootstrap Generalisation Error Estimate for Stochastic Discrimination and Random Forests

Prior, Matthew; Windeatt, Terry

doi:10.1109/icpr.2006.913

Cited by 2 publications

(1 citation statement)

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“…The implementation of stochastic discrimination described in detail in [9,12] uses a method of uniformity enforcement that is based on measurements relating to the average coverage for elements of the ensemble predicting a specific class. We propose an alternative uniformity enforcement scheme based on the minimum instance coverage.…”

Section: Introductionmentioning

confidence: 99%

Improved Uniformity Enforcement in Stochastic Discrimination

Prior

Windeatt

2009

Multiple Classifier Systems

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There are a variety of methods for inducing predictive systems from observed data. Many of these methods fall into the field of study of machine learning. Some of the most effective algorithms in this domain succeed by combining a number of distinct predictive elements to form what can be described as a type of committee. Well known examples of such algorithms are AdaBoost, bagging and random forests. Stochastic discrimination is a committee-forming algorithm that attempts to combine a large number of relatively simple predictive elements in an effort to achieve a high degree of accuracy. A key element of the success of this technique is that its coverage of the observed feature space should be uniform in nature. We introduce a new uniformity enforcement method, which on benchmark datasets, leads to greater predictive efficiency than the currently published method.

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Section: Introductionmentioning

confidence: 99%