Evaluation of particle swarm optimization effectiveness in classification (Contributo in atti di convegno)

Type
Label
  • Evaluation of particle swarm optimization effectiveness in classification (Contributo in atti di convegno) (literal)
Anno
  • 2006-01-01T00:00:00+01:00 (literal)
Alternative label
  • De Falco I.; Della Cioppa A.; Tarantino E. (2006)
    Evaluation of particle swarm optimization effectiveness in classification
    in 6th International Workshop on Fuzzy Logic and Applications, Crema, Italy, September 15-17, 2005
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • De Falco I.; Della Cioppa A.; Tarantino E. (literal)
Pagina inizio
  • 164 (literal)
Pagina fine
  • 171 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#titoloVolume
  • Fuzzy Logic and Applications (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#volumeInCollana
  • 3849 (literal)
Note
  • ISI Web of Science (WOS) (literal)
  • Scopu (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Institute of High Performance Computing and Networking, National Research Council of Italy (ICAR-CNR), Via P. Castellino 111, 80131 Naples - Italy Natural Computation Lab - DIIIE, University of Salerno, Via Ponte Don Melillo 1, 84084 Fisciano (SA) - Italy (literal)
Titolo
  • Evaluation of particle swarm optimization effectiveness in classification (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
  • 978-3-540-32529-1 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#curatoriVolume
  • Bloch I.; Petrosino A.; Tettamanzi A.G.B. (literal)
Abstract
  • Particle Swarm Optimization (PSO) is a heuristic optimization technique showing relationship with Evolutionary Algorithms and strongly based on the concept of swarm. It is used in this paper to face the problem of classification of instances in multiclass databases. Only a few papers exist in literature in which PSO is tested on this problem and there are no papers showing a thorough comparison for it against a wide set of techniques typically used in the field. Therefore in this paper PSO performance is compared on nine typical test databases against those of nine classification techniques widely used for classification purposes. PSO is used to find the optimal positions of class centroids in the database attribute space, via the examples contained in the training set. Performance of a run, instead, is computed as the percentage of instances of testing set which are incorrectly classified by the best individual achieved in the run. Results show the effectiveness of PSO, which turns out to be the best on three out of the nine challenged problems. (literal)
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