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Genetic nearest feature plane
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DOI：10.1016/j.eswa.2007.10.009.png)
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Abstract
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The problem addressed in this paper concerns the complexity reduction of the nearest feature plane classifier, so that it may be applied also in dataset where the training set contains many patterns. This classifier considers, to classify a test pattern, the subspaces created by each combination of three training patterns. The main problem is that in dataset of high cardinality this method is unfeasible. A genetic algorithm is here used for dividing the training patterns in several clusters which centroids are used to build the feature planes used to classify the test set. The performance improvement with respect to other nearest neighbor based classifiers is validated through experiments with several benchmark datasets. (C) 2007 Elsevier Ltd. All rights reserved.
Keywords:
Nearest feature plane classifier
Clustering
Genetic algorithm
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