Improving classification Accuracy of Neural Network through Clustering Algorithms
| ||International Journal of Computer Trends and Technology (IJCTT)|| |
|© - September Issue 2013 by IJCTT Journal|
|Volume-4 Issue-9 |
|Year of Publication : 2013|
|Authors :B.Madasamy, Dr.J.Jebamalar Tamilselvi|
B.Madasamy, Dr.J.Jebamalar Tamilselvi"Improving classification Accuracy of Neural Network through Clustering Algorithms "International Journal of Computer Trends and Technology (IJCTT),V4(9):3242-3246 September Issue 2013 .ISSN 2231-2803.www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract:- A common problem in bio medical data using neural networks for classification purposes are complex nature of the data, high dimensionality, convoluted and overlapping classes with their remarkable ability to derive meaning from complicated data, can be extract patterns and trends are too complex. Bio medical classification is a complex process to make decisions. Classification performance of the neural network suffers dramatically. This paper proposes neural network and data mining techniques are combined to automate biomedical classification processes to support decision. To improve the classification ability and behavior of neural network is used by pre-processing and pre-clustered data with the help of Rule based induction, Multi-layer perceptron model, nearest neighbor, Radial basics function and back propagation learning algorithm is employed to classify such complex tasks. The proposed clustering algorithm applied to the bio medical dataset to reduce the amount of samples to be presented to the neural network. It improves accuracy and computation time when applied to the publicly available benchmark bio medical dataset.
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Keywords — : Neural Network, Data mining, back propagation, Multilayer perceptron.