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Gene Expression Based Acute Leukemia Cancer Classification: A Neuro-Fuzzy Approach
B.B.M.Krishna Kanth, U.V.kulkarni, B.G.V.Giridhar
Pages - 136 - 146     |    Revised - 30-08-2010     |    Published - 30-10-2010
Volume - 4   Issue - 4    |    Publication Date - September 2010  Table of Contents
gene expression data, cancer classification, membership function, AAL/AML
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.
CITED BY (3)  
1 Kaushik, A. (2014). M. Tech. Thesis (Doctoral dissertation, punjab technical university).
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.
3 Rosales-Pérez, A., Reyes-García, C. A., Gómez-Gil, P., Gonzalez, J. A., & Altamirano, L. (2011). Genetic selection of fuzzy model for acute leukemia classification. In Advances in Artificial Intelligence (pp. 537-548). Springer Berlin Heidelberg.
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Mr. B.B.M.Krishna Kanth
S.R.T.M.University - India
Dr. U.V.kulkarni
S.R.T.M.University - India
Dr. B.G.V.Giridhar
Andhra Medical College - India