RT Conference Proceedings
T1 Building nearest prototype classifiers using a Michigan approach PSO
A1 Cervantes, Alejandro
A1 Galván, Inés M.
A1 Isasi, Pedro
AB This paper presents an application of particle swarm optimization (PSO) to continuous classification problems, using a Michigan approach. In this work, PSO is used to process training data to find a reduced set of prototypes to be used to classify the patterns, maintaining or increasing the accuracy of the nearest neighbor classifiers. The Michigan approach PSO represents each prototype by a particle and uses modified movement rules with particle competition and cooperation that ensure particle diversity. The result is that the particles are able to recognize clusters, find decision boundaries and achieve stable situations that also retain adaptation potential. The proposed method is tested both with artificial problems and with three real benchmark problems with quite promising results.
PB IEEE
SN 1-4244-0708-7
YR 2007
FD 2007-04
LK https://hdl.handle.net/10016/4014
UL https://hdl.handle.net/10016/4014
LA eng
NO IEEE Swarm Intelligence Symposium. Honolulu, HI, 1-5 april 2007
DS e-Archivo
RD 24 may. 2024