Publication:
Binary particle swarm optimization in classification

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URI: https://hdl.handle.net/10016/4424
ISSN: 1210-0552
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binary_galvan_NNW_2005.pdf (1.51 MB)
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2005
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Institute of Computer Science, Academy of Sciences of the Czech Republic
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Abstract
Purpose of this work is to show that the Particle Swarm Optimization Algorithm may improve the results of same well known Machine Learning methods in the resolution of discrete classification problems. A binary version of the PSO algorithm is used to obtain a set of logic rules that map binary masks (that represent the attribute values), lo the available classes. This algorithm has been tested both in a single pass mode and in an iterated mode on a well-known set of problems, called the MONKS set, lo compare the PSO results against the results reported for that domain by the application of some common Machine Learning algorithms.
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Evolutionary computation, Particle swarm optimization, Pattern classification, Swarm intelligence
Bibliographic citation
Neural Network World, 2005, vol. 15, n.3, p. 229 - 241
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DI - GCERN - Artículos de revistas científicas