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Classification by evolutionary ensembles
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DOI：10.1016/j.patcog.2005.09.016.png)
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Abstract
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This paper is about building an ensemble of classifiers each of which is trained based on a particular weighting over the training examples (a weighting is a set of weights associated with the examples). The task concerns search in a tremendous weighting space. In this view we propose to incorporate a genetic algorithm (GA). It performs a wide yet efficient search for appropriate weightings (chromosomes). The difference from a traditional GA is that all the weightings throughout evolution Will be exploited to form the final ensemble, not just the best weighting. Our algorithm is tested on the UCI benchmark data sets and used to design a face detection system. Robust and consistently accurate classification is experienced. Comparative results with two other algorithms, i.e. AdaBoost and Bagging, are also given. (c) 2005 Published by Elsevier Ltd on behalf of Pattern Recognition Society.
Keywords:
multiple classifier system
genetic algorithms
evolutionary learning
classifier combination
AdaBoost
bagging
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