Cat Swarm based Optimization of Gene Expression Data Classification
| ||International Journal of Computer Trends and Technology (IJCTT)|| |
|© - May Issue 2013 by IJCTT Journal|
|Volume-4 Issue-5 |
|Year of Publication : 2013|
|Authors :Amit Kumar, Debahuti Mishra|
Amit Kumar, Debahuti Mishra"Cat Swarm based Optimization of Gene Expression Data Classification"International Journal of Computer Trends and Technology (IJCTT),V4(5):1185-1190 May Issue 2013 .ISSN 2231-2803.www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract: - An Artificial Neural Network (ANN) does have the capability to provide solutions of various complex problems. The generalization ability of ANN due to the massively parallel processing capability can be utilized to learn the patterns discovered in the data set which can be represented in terms of a set of rules. This rule can be used to find the solution to a classification problem. The learning ability of the ANN is degraded due to the high dimensionality of the datasets. Hence, to minimize this risk we have used Principal Component Analysis (PCA) and Factor Analysis (FA) which provides a feature reduced dataset to the Multi Layer Perceptron (MLP), the classifier used. Again, since the weight matrices are randomly initialized, hence, in this paper we have used Cat Swarm Optimization (CSO) method to update the weight values of the weight matrix. From the experimental evaluation, it was found that using CSO with the MLP classifier provides better classification accuracy as compared to when the classifier is solely used.
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Keywords — Classification, Artificial neural network, Multi-layer perceptron, Principal component analysis, Factor analysis, Cat swarm optimization.