Particle Swarm Intelligence to Optimize the Learning of N-tuples

Abstract

This paper concentrates on the swarm intelligence based bio-inspired approach to optimize N-tuple classifiers. We will explain the implementation of the particle swarm (PS) based training for N-tuple classifier that helps to recognize patterns more efficiently by learning the good sets of N-tuples. Various versions of the particle swarm based training will be explored by changing different parameters of the system. The swarm algorithm will also be hybridized with other bio-inspired techniques like Self-Organized Criticality (SOC) and the Fitness to Distance Ratio (FDR) based solection to add diversity in the swarm population. Both hybrid and pure particle swarm based training will be compared against standard algorithms like random sampling, the hill-climbing type stochastic methods, and genetic algorithm (BA) based approach. Results willb e shown on a subset of the NIST handwritten character set. This paper will describe in detail how particle swarm can be modeled to train an N-tuple classifier and how various parameters of the system should be chosen carefully to obtain an optimum set of tupes that achieves better recognition.