Research Article | OPEN ACCESS
A Novel Optimized Adaptive Learning Approach of RBF on Biomedical Data Sets
1Anusuya S. Venkatesan and 2Latha Parthiban
1Department of Information Technology, Saveetha School of Engineering, Saveetha University, Thiruvallur, Tamil Nadu-602105, India
2Pondicherry University, Puducherry, India
Research Journal of Applied Sciences, Engineering and Technology 2014 4:541-547
Received: April 01, 2014 | Accepted: May 19, 2014 | Published: July 25, 2014
Abstract
In this study, we propose a novel learning approach of Radial Basis Function Neural Network (RBFNN) based on Fuzzy C-Means (FCM) and Quantum Particle Swarm Optimization (QPSO) to group similar data. The performance of RBFNN relies on the parameters such as number of hidden nodes, centres and width of Gaussian function and weight matrix between hidden layer and output layer. Generally, RBF is trained with a fixed number of nodes but in this study we allow the network to have variable number of hidden nodes based on the size of input samples. The clustering algorithm, Fuzzy C Means (FCM) is optimized with QPSO to provide global optimal centres for RBFNN. The weights are calculated by using Least Square Method and the root mean square error is optimized to improve the accuracy, accordingly the hidden unit numbers are adjusted. The cluster centres are obtained using optimized FCM and are checked against random selection of centres to verify the suitability. The datasets such as liver disorder and breast cancer from UCI machine learning repository are used for the experiments. The accuracy is analyzed for the Cluster Numbers (CN) 2, 3, 4, 5, 6, 7, 8, 9, 10, 15 and 20, respectively.
Keywords:
FCM , fitness , QPSO , RBFNN , RMSE,
References
-
Adibi, P., M.R. Meybodi and R. Safabakhsh, 2005. Unsupervised learning of synaptic delays based on learning automata in an RBF-like network of spiking neurons for data clustering. Neurocomputing, 64: 335-357.
CrossRef
-
Antonino, S., T. Roberto and P. Witold, 2006. Improving RBF networks performance in regression tasks by means of a supervised fuzzy clustering. Neurocomputing, 69(13-15): 1570-1581.
CrossRef
-
Antonios, D.N. and E.T. George, 2012. A novel training algorithm for RBF neural network using a hybrid fuzzy clustering approach. Fuzzy Set. Syst., 193: 62-84.
CrossRef
-
Bo, L. and F. Jiulun, 2008. Parameter selection of generalized fuzzy entropy-based thresholding method with quantum-behavior particle swarm optimization. Proceeding of the International Conference on Audio, Language and Image Processing, pp: 546-551.
-
Debao, C., W. Jiangtao, Z. Feng, H. Weibo and Z. Chunxia, 2012. An improved group search optimizer with operation of quantum-behaved swarm and its application. Appl. Soft Comput., 12(2): 712-725.
CrossRef
-
Denis, F., L. Evelina, G. Giacomo, F. Michele and D. Raffaele, 2011. Integrating clustering and classification techniques: A case study for reservoir facies prediction. Stud. Comput. Intell., 369: 21-34.
-
Er, M.J., W. Chen and S. Wu, 2005. High-speed face recognition based on discrete cosine transform and RBF neural networks. IEEE T. Neural Networ., 16(3): 679-6915.
CrossRef PMid:15940995
-
George, E. and J. Tsekouras, 2013. On training RBF neural networks using input-output fuzzy clustering and particle swarm optimization. Fuzzy Set. Syst., 221: 65-89.
CrossRef
-
Grisales, V.H., J.J. Soriano, S. Barato and D.M. Gonzalez, 2004. Robust agglomerative clustering algorithm for fuzzy modeling purposes. Proceeding of the American Control Conference. Boston, 2: 1782-1787.
-
Harun, P., E. Burak, P. Andy and Y. Çetin, 2012. Clustering of high throughput gene expression data. Comput. Oper. Res., 39(12): 3046-3061.
CrossRef PMid:23144527 PMCid:PMC3491664
-
Jun, S., C. Wei, F.Wei, W. Xiaojun and X. Wenbo, 2012. Gene expression data analysis with the clustering method based on an improved quantum-behaved particle swarm optimization. Eng. Appl. Artif. Intel., 25(2): 376-391.
CrossRef -
Kennedy, J. and R.C. Eberhart, 1995. Particle Swarm Optimization. Proceedings of the IEEE International Conference on Neural Network. Perth, WA, 4: 1942-1948.
CrossRef
-
Leandro dos Santos, C., 2010. Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems. Expert Syst. Appl., 37(2): 1676-1683.
CrossRef
-
Leung, S.Y.S., T. Yang and W.K. Wong, 2012. A hybrid particle swarm optimization and its application in neural networks. Expert Syst. Appl., 39(1): 395-405.
CrossRef -
Liu, F., C. Sun, W. Si-Ma, R. Liao and F. Guo, 2006. Chaos control of ferroresonance system based on RBF-maximum entropy clustering algorithm. Phys. Lett. A, 357(3): 218-223.
CrossRef
-
Mahdi, A., N. Ali Nedaie and A. Alimohammad, 2013. Presentation of clustering-classification heuristic method for improvement accuracy in classification of severity of road accidents in Iran. Safety Sci., 60: 142-150.
CrossRef
-
Mehmet, K. and D. Berat, 2010. ECG beat classification using particle swarm optimization and radial basis, function neural network. Expert Syst. Appl., 37(12): 7563-7569.
CrossRef
-
Min, H. and X. Jianhui, 2004. Efficient clustering of radial basis perceptron neural network for pattern recognition. Pattern Recogn., 37(10): 2059-2067.
CrossRef
-
Moody, J. and C.J. Darken, 1989. Fast learning in network of locally-tuned processing units. Neural Computation, 1: 281-294.
CrossRef -
Sun, J., W.B. Xu and B. Feng, 2004. A global search strategy of quantum-behaved particle swarm optimization. Proceeding of the IEEE Conference on Cybernetics and Intelligent Systems, pp: 111-116.
-
Vahid, F. and M. Gholam Ali, 2013. An improvement in RBF learning algorithm based on PSO for real time applications. Neurocomputing, 111: 169-176.
CrossRef
-
Xiao-Yuan, J., Y. Yong-Fang, Z. David, Y. Jing-Yu and L. Miao, 2007. Face and palmprint pixel level fusion and Kernel DCV-RBF classifier for small sample biometric recognition. Pattern Recogn., 40(11): 3209-3224.
CrossRef
-
Xin-Zheng, X., D. Shi-Fei, S. Zhong-Zhi and Z. Hong, 2012. Optimizing radial basis function neural network based on rough sets and affinity propagation clustering algorithm. Comput. Electr., 13(2): 131-138.
-
Zhen, Z., Z. Wang, Y. Hu and M. Geng, 2008. Learning method of RBF network based on FCM and ACO. Proceeding of the Chinese Control Decision Conference, pp: 102-105.
-
Zhide, L., C. Jiabin and S. Chunlei, 2009. A new RBF neural network with GA-based fuzzy C-means clustering algorithm for SINS fault diagnosis. Proceeding of the Chinese Control and Decision Conference, pp: 208-221.
CrossRef
Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
|
|
|
ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
|
Information |
|
|
|
Sales & Services |
|
|
|