Abstract
In this paper we propose a particle swarm based back propagation neural network model which uses an optimized target to maximize the classification accuracy of the classifier. By using Particle swarm optimization technique an optimized target for each class was determined and there after the artificial neural network is used to classify the data using these targets. For this, some of the bench mark classification datasets are used, which are taken from UCI learning repository. An extensive experimental study has been carried out to compare the proposed method and existing method on the same datasets and a comparative analysis is done by taking several parameters like percentage of accuracy, time of response and complexity of the algorithm. During this study we have examined the performance improvement of the proposed PSO and BPN combined approach over the conventional BPN approach to generate classification inferences from the training and testing results.
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Dash, N., Priyadarshini, R., Misra, R. (2015). An ANN Model to Classify Multinomial Datasets with Optimized Target Using Particle Swarm Optimization Technique. In: Jain, L., Behera, H., Mandal, J., Mohapatra, D. (eds) Computational Intelligence in Data Mining - Volume 1. Smart Innovation, Systems and Technologies, vol 31. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2205-7_34
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DOI: https://doi.org/10.1007/978-81-322-2205-7_34
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