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 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.


[1] Shelly Gupta, Dharminder Kumar, Anand Sharma, Performance analysis of various data mining classification techniques on healthcare data,International Journal of Computer Science & Information Technology (IJCSIT 2011),Vol 3, pp .155-169.
[2] PeimanMamaniBarnaghi,VahidAlizadehSahzabi,Azuraliza Abu Bakar, A Comparative Study for Various Methods of Classification,International Conference on Information and Computer Networks (ICICN 2012), vol. 27, pp. 62-66.
[3] Zuyi Wang1, Yue Wang, JianhuaXuan, Yibin Dong, Marina Bakay, Yuanjian Feng3 Robert Clarke, Eric P. Hoffman,Optimized multilayer perceptrons for molecular classi?cation and diagnosis using genomic data, Oxford University Press 2006, vol. 22,pp.755–761.
[4] John Paul T. Yusiong , Optimizing Artificial Neural Networks Using Cat Swarm Optimization Algorithm, I.J. Intelligent Systems and Applications, 2012; pp. 69-80.
[5] JianweiNiu, Yiling He, Muyuan Li, Xin Zhang, Linghua Ran, Chuzhi Chao, Baoqin Zhang, A Comparative Study on Application of Data Mining Technique in Human Shape Clustering: Principal Component Analysis vs. Factor Analysis, 5th IEEE Conference on Industrial Electronics and Applications, 2010,pp 2014-2018.
[6] S.C.Chu, and P. W.Tsai, Computational intelligence based on the behaviour of cat, International journal of Innovative Computing, Information and Control, 3, vol. 1, pp.163-173, 2007.
[7] Ajay Kumar Tanwani, Jamal Afridi, M. ZubairSha?q,MuddassarFarooq, Guidelines to Select Machine Learning Scheme for Classi?cation of Biomedical Dataset, EvoBIO:Springer LNCS 2009, 5483, pp. 128–139.
[8] Ng Ee Ling, Yahya Abu Hasan, Classification On Microarray Data, Regional Conference on Mathematics,Statistics and Applications, 2006; pp.1-8.
[9] Guo-zheng Li , Hua-Long Bu, Mary Qu Yang,Xue-QiangZeng,Jack Y Yang,Selecting subsets of newly extracted features from PCA and PLS in microarray data analysis,IEEE 7th international conference on Bioinformatics and Bioengineering, 2007,pp.1-15.
[10] L.Ladha, T.Deepa,Feature selection methods and algorithms, International Journal on Computer Science and Engineering (IJCSE) 2011,vol. 3 no. 5,pp.1787-1797.

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