Michigan Particle Swarm Optimization for Prototype Reduction in Classification Problems


This paper presents a new approach to Particle Swarm Optimization, called Michigan Approach PSO (MPSO), and its applica- tion to continuous classi cation problems as a Nearest Prototype (NP) classi er. In Nearest Prototype classi ers, a collection of prototypes has to be found that accurately represents the input patterns. The classi er then assigns classes based on the nearest prototype in this collection. The MPSO algorithm is used to process training data to nd those prototypes. In the MPSO algorithm each particle in a swarm represents a single pro- totype in the solution and it uses modi ed movement rules with particle competition and cooperation that ensure particle diversity. The proposed method is tested both with arti cial problems and with real benchmark problems and compared with several algorithms of the same family. Re- sults show that the particles are able to recognize clusters, nd decision boundaries and reach stable situations that also retain adaptation po- tential. The MPSO algorithm is able to improve the accuracy of 1-NN classi ers, obtains results comparable to the best among other classi ers, and improves the accuracy reported in literature for one of the problems.Publicad

Similar works

Full text


Universidad Carlos III de Madrid e-Archivo

Provided a free PDF time updated on 10/2/2015View original full text link

This paper was published in Universidad Carlos III de Madrid e-Archivo.

Having an issue?

Is data on this page outdated, violates copyrights or anything else? Report the problem now and we will take corresponding actions after reviewing your request.