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ANNPR
2008
Springer

Combining Methods for Dynamic Multiple Classifier Systems

14 years 26 days ago
Combining Methods for Dynamic Multiple Classifier Systems
Most of what we know about multiple classifier systems is based on empirical findings, rather than theoretical results. Although there exist some theoretical results for simple and weighted averaging, it is difficult to gain an intuitive feel for classifier combination. In this paper we derive a bound on the region of the feature space in which the decision boundary can lie, for several methods of classifier combination using non-negative weights. This includes simple and weighted averaging of classifier outputs, and allows for a more intuitive understanding of the influence of the classifiers combined. We then apply this result to the design of a multiple logistic model for classifier combination in dynamic scenarios, and discuss its relevance to the concept of diversity amongst a set of classifiers. We consider the use of pairs of classifiers trained on label-swapped data, and deduce that although non-negative weights may be beneficial in stationary classification scenarios, for dyna...
Amber Tomas
Added 12 Oct 2010
Updated 12 Oct 2010
Type Conference
Year 2008
Where ANNPR
Authors Amber Tomas
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