Particle Swarm Optimisation of Multiple Classifier Systems
In this paper we present application of various versions of the particle swarm optimization method (PSO) in the process of generation of multiple-classifier systems (MCS). While some of the investigated optimisation problems naturally lend themselves to the type of optimisation for which PSO is most suitable we present some other applications requiring non-standard representation of the particles as well as handling of constraints in the optimisation process. In the most typical optimisation case the continuous version of PSO has been successfully applied for the optimization of a soft-linear combiner. On the other hand, one of the adapted binary versions of PSO has been shown to work well in the case of multi-stage organization of majority voting (MOMV), where the search dimension is high and the local search techniques can often get stuck in local optima. All three presented PSO based methods have been tested and compared to each other and to forward search and stochastic hillclimber for five real-world non-trivial datasets.
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