Swarm intelligence
Advances in Instance Selection for Instance-Based Learning Algorithms
Data Mining and Knowledge Discovery
Dynamic Search With Charged Swarms
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Evolutionary Design of Nearest Prototype Classifiers
Journal of Heuristics
Don't push me! Collision-avoiding swarms
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Classifier fitness based on accuracy
Evolutionary Computation
Differential Evolution Classifier in Noisy Settings and with Interacting Variables
Applied Soft Computing
Particle swarm classification: A survey and positioning
Pattern Recognition
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Nearest Prototype methods can be quite successful on many pattern classification problems. In these methods, a collection of prototypes has to be found that accurately represents the input patterns. The classifier then assigns classes based on the nearest prototype in this collection. In this paper we develop a new algorithm (called AMPSO), based on the Particle Swarm Optimization (PSO) algorithm, that can be used to find those prototypes. Each particle in a swarm represents a single prototype in the solution; the swarm evolves using modified PSO equations with both particle competition and cooperation. Experimentation includes an artificial problem and six common application problems from the UCI data sets. The results show that the AMPSO algorithm is able to find solutions with a reduced number of prototypes that classify data with comparable or better accuracy than the 1-NN classifier. The algorithm can also be compared or improves the results of many classical algorithms in each of those problems; and the results show that AMPSO also performs significantly better than any tested algorithm in one of the problems.