The proposal of a Constructive Particle Swarm Classifier

The data classification task is one of the main tasks within the knowledge discovering from databases (KDD). Its goal is to allow the correct classification of new objects (records from a database), unknown to the classifier, based upon the extraction of knowledge from objects known a priori. These data already known can be used to generate a classification model, or simply to infer the class of new objects, from those whose classes are known. This paper presents a proposal for a classification algorithm, called Constructive Particle Swarm Classifier (cPSClass), which uses mechanisms from the Particles Swarm Clustering algorithm and Artificial Immune Systems to determine dynamically the number of prototypes from a database and use them to predict the correct class to which a new input object should belong. For performance evaluation the cPSClass was applied to some datasets from the literature and its performance was compared with its predecessor version, the non constructive Particle Swarm Classifier, and also the Naïve Bayes algorithm.