A condensed polynomial neural network for classification using swarm intelligence

Authors:
S. Dehuri;B. B. Misra;A. Ghosh;S. -B. Cho
Affiliations:
Department of Information and Communication Technology, Fakir Mohan University, Vyasa Vihar, Balasore 756019, Orissa, India;Department of Computer Science and Engineering, College of Engineering, Bhubaneswar 751024, Orissa, India;Machine Intelligence Unit and Center for Soft Computing Research, Indian Statistical Institute Kolkata, 203 B.T. Road, Kolkata 700108, India;Soft Computing Laboratory, Department of Computer Science, Yonsei University, 134 Shinchon-dong, Sudaemoon-ku, Seoul 120-749, Republic of Korea
Venue:
Applied Soft Computing
Year:
2011

Citing 8
Cited 1

Adaptive pattern recognition and neural networks

Adaptive pattern recognition and neural networks
A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-Three Old and New Classification Algorithms

Machine Learning
Inductive Learning Algorithms for Complex Systems Modeling

Inductive Learning Algorithms for Complex Systems Modeling
Parameter Selection in Particle Swarm Optimization

EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Comparison between Genetic Algorithms and Particle Swarm Optimization

EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
Particle swarm for attribute selection in Bayesian classification: an application to protein function prediction

Journal of Artificial Evolution and Applications - Particle Swarms: The Second Decade
A reduced and comprehensible polynomial neural network for classification

Pattern Recognition Letters

A comprehensive machine learning approach to prognose pulmonary disease from home

Proceedings of the International Conference on Advances in Computing, Communications and Informatics

Quantified Score

Hi-index	0.00

Visualization

Abstract

A novel condensed polynomial neural network using particle swarm optimization (PSO) technique is proposed for the task of classification in this paper. In solving classification task classical algorithms such as polynomial neural network (PNN) and its variants need more computational time as the partial descriptions (PDs) grow over the training period layer-by-layer and make the network very complex. Unlike PNN the proposed network needs to generate the partial description for a single layer. The discrete PSO (DPSO) is used to select a relevant set of PDs as well as features with a hope to get better accuracy, which are in turn fed to the output neuron. The weights associated with the links from hidden to output neuron is optimized by PSO for continuous domain (CPSO). Performance of this model is compared with the results obtained from PNN. Simulation result shows that the performance of this model both in processing time and accuracy, is encouraging for harnessing its power in domain with large and complex data particularly in data mining area.