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Gene Expression Based Acute Leukemia Cancer Classification: A Neuro-Fuzzy Approach
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International Journal of Biometrics and Bioinformatics (IJBB)
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Volume:  4    Issue:  4
Pages:  136-160
Publication Date:   September 2010
ISSN (Online): 1985-2347
Pages 
136 - 146
Author(s)  
 
Published Date   
30-10-2010 
Publisher 
CSC Journals, Kuala Lumpur, Malaysia
ADDITIONAL INFORMATION
Keywords   Abstract   References   Cited by   Related Articles   Collaborative Colleague
 
KEYWORDS:   gene expression data, cancer classification, membership function, AAL/AML 
 
 
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In this paper, we proposed the Modified Fuzzy Hypersphere Neural Network (MFHSNN) for the discrimination of acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) in leukemia dataset. Dimensionality reduction methods, such as Spearman Correlation Coefficient and Wilcoxon Rank Sum Test are used for gene selection. The performance of the MFHSNN system is encouraging when benchmarked against those of Support vector machine (SVM) and the K-nearest neighbor (K-NN) classifiers. A classification accuracy of 100% has been achieved using the MFHSNN classifier using only two genes. Furthermore, MFHSNN is found to be much faster with respect to training and testing time. 
 
 
 
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1 S. S. Chowhan, U. V. Kulkarni and G. N. Shinde (2011), “Iris Recognition Using Modified Fuzzy HypersphereNeural Network with Different Distance Measures”, International Journal of Advanced Computer Science and Applications, 2(6), pp. 130-134, 2011.
2 Alejandro Rosales-Pérez, Carlos A. Reyes-García, Pilar Gómez-Gil, Jesus A. Gonzalez , Leopoldo Altamirano (2011), “Genetic Selection of Fuzzy Model for Acute Leukemia Classification ”, Advances in Artificial Intelligence Lecture Notes in Computer Science, Vol. 7094/2011, pp. 537-548, 2011.
 
 
 
1 Shri Guru Gobind Singhji Institute of Engineering and Technology
 
 
 
B.B.M.Krishna Kanth : Colleagues
U.V.kulkarni : Colleagues
B.G.V.Giridhar : Colleagues  
 
 
 
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