PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Improved Uniformity Enforcement in Stochastic Discrimination, 8th Int. Workshop Multiple Classifier Systems, Iceland, June 2009, Lecture notes in computer science,
M Prior and Terry Windeatt
In: MS 2009, Iceland(2009).


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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EPrint Type:Conference or Workshop Item (Oral)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:6069
Deposited By:Terry Windeatt
Deposited On:08 March 2010