An Evolutionary Framework Using Particle Swarm Optimization for Classification Method PROAFTN

Download
  1. (PDF, 325 KB)
  2. Get@NRC: An Evolutionary Framework Using Particle Swarm Optimization for Classification Method PROAFTN (Opens in a new window)
DOIResolve DOI: http://doi.org/10.1016/j.asoc.2011.06.003
AuthorSearch for: ; Search for: ; Search for: ; Search for:
TypeArticle
Journal titleApplied Soft Computing
Volume11
Issue8
Pages4971–4980
SubjectKnowledge Discovery; Particle Swarm Optimization; MCDA; PROAFTN; Classification
AbstractThe aim of this paper is to introduce a methodology based on the particle swarm optimization (PSO) algorithm to train the Multi-Criteria Decision Aid (MCDA) method PROAFTN. PSO is an efficient evolutionary optimization algorithm using the social behavior of living organisms to explore the search space. It is a relatively new population-based metaheuristic that can be used to find approximate solutions to difficult optimization problems. Furthermore, it is easy to code and robust to control parameters. To apply PROAFTN, the values of several parameters need to be determined prior to classification, such as boundaries of intervals and weights. In this study, the proposed technique is named PSOPRO, which utilizes PSO to elicit the PROAFTN parameters from examples during the learning process. To test the effectiveness of the methodology and the quality of the obtained models, PSOPRO is evaluated on 12 public-domain datasets and compared with the previous work applied on PROAFTN. The computational results demonstrate that PSOPRO is very competitive with respect to the most common classification algorithms.
Publication date
PublisherElsevier
LanguageEnglish
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedYes
NPARC number19298567
Export citationExport as RIS
Report a correctionReport a correction
Record identifierab0d4beb-dfd6-4ec8-97e3-2f052515ccdd
Record created2012-01-24
Record modified2016-05-09
Bookmark and share
  • Share this page with Facebook (Opens in a new window)
  • Share this page with Twitter (Opens in a new window)
  • Share this page with Google+ (Opens in a new window)
  • Share this page with Delicious (Opens in a new window)