International Journal of Computer
Trends and Technology

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Volume 4 | Issue 5 | Year 2013 | Article Id. IJCTT-V4I5P44 | DOI : https://doi.org/10.14445/22312803/IJCTT-V4I5P44

Cat Swarm based Optimization of Gene Expression Data Classification

Amit Kumar, Debahuti Mishra

Citation :

Amit Kumar, Debahuti Mishra, "Cat Swarm based Optimization of Gene Expression Data Classification," International Journal of Computer Trends and Technology (IJCTT), vol. 4, no. 5, pp. 1185-1190, 2013. Crossref, https://doi.org/10.14445/22312803/IJCTT-V4I5P44

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.

Keywords

Classification, Artificial neural network, Multi-layer perceptron, Principal component analysis, Factor analysis, Cat swarm optimization.

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