Rule based Hybrid Weighted Fuzzy Classifier for Tumor Data
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2018-11-27 https://doi.org/10.14419/ijet.v7i4.19.22030
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Data mining, classificaton, Bioinformatics, Fuzzy sytems, genetic algorithms, weighted rule. -
Abstract
Examination of gene based information has turned out to be so essential in biomedical industry for assurance of basic ailments. A fuzzy rule based classification is a standout amongst the most mainstream approaches utilized as a part of example arrangement issues. The fuzzy rule based classifier creates an arrangement of fuzzy if-then decides that empower exact non-straight order of information designs. In spite of the fact that there are different techniques to create fluffy if-then guidelines, the advancement of lead producing process is as yet an issue. Here, we introduce a half and half weighted fluffy order framework in which few fluffy if-then principles are chosen by methods for offering weights to preparing designs. Further, we utilize a genetic algorithm (GA) to streamline the classifier for quality articulation investigation
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References
[1] T. R. Golub, D. K. Slonim, P. Tamayo, C. Huard, M. Gaasenbeek, J. P. Mesirov, H. Coller, M. L. Loh, J. R. Downing, M. A. Caligiuri, C. D. Bloomfield, and E. S. Lander, “Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring,†Science, vol. 286, pp. 531–537, 1999.
[2] Pascale Anderle, Manuel Duval, Sorin Draghici, Alexander Kuklin, Timothy G. Littlejohn, Juan F.Medrano, David Vilanova, and Matthew Alan Roberts “Gene Expression Databases and Data Mining†Biotechniques. 2003 Mar; Suppl: 36-44.
[3] Gerald Schaefer, Tomoharu Nakashima, “Data Mining of Gene Expression Data by Fuzzy and Hybrid Fuzzy Methods†IEEE Trans. Information Technology in Biomedicine, vol. 14, pp. 23-29, 2010.
[4] H. Ishibuchi and T. Nakashima, “Improving the performance of fuzzy classifier systems for pattern classification problems with continuous attributes,†IEEE Trans. Ind. Electron., vol. 46, no. 6, pp. 1057–1068, Dec. 1999.
[5] Jiawei Han and Micheline Kamber, “Data Mining: Concepts and Techniquesâ€, 2nd edition, ISBN: 978-55860-901-3, Elsevier.
[6] Max Bramer, “Principles of Data Miningâ€, ISBN: 978-81-8489-166-9, springer, 2007.
[7] Tomoharu Nakashima, Yasuyuki Yokota, Hisao Ishibuchi, “Constructing fuzzy classification systems from weighted training patternsâ€, in proc. 19th European conf. on modeling and simulation, vol 3, pp.2386-2391, 2004.
[8] H. Ishibuchi and T. Nakashima, “Effect of rule weights in fuzzy rule-based classification systems,†IEEE Trans. Fuzzy Syst., vol. 9, no. 4,pp. 506–515, Aug. 2001.
[9] P.Woolf and Y.Wang, “A fuzzy logic approach to analyzing gene expression data,†Physiol. Genomics, vol. 3, pp. 9–15, 2000.
[10] C. Z. Janikow, “A genetic algorithm for optimizing fuzzy decision trees,†in Proc. 6th Int. Conf. Genetic Algorithms, Univ. Pittsburgh, Pittsburgh, PA, July 15–19, 1995, pp. 421–428.
[11] S.Sheeba Rani, R.Maheswari, V.Gomathy and P.Sharmila, “Iot driven vehicle license plate extraction approach†in International Journal of Engineering and Technology(IJET) , Volume.7, 2018, pp 457-459, April 2018
[12] M.A.Lee, H.Takagi, “Dynamic control of genetic algorithms using fuzzy logic techniques,†in Pmc.Int.Conf. Genetic Algorithm,Urbana-Champaign,lL,July 1993,pp.76-83.
[13] Zhun-Ga Liu, Quan Pan, Jean Dezert, “Hybrid Classification System for Uncertain Dataâ€, IEEE Transactions on Systems, Man, and Cybernetics: Systems ( Volume: 47, Issue: 10, Oct. 2017 ).
[14] Balakrishnan S, K.Aravind, A. Jebaraj Ratnakumar, “A Novel Approach for Tumor Image Set Classification Based On Multi-Manifold Deep Metric Learningâ€, International Journal of Pure and Applied Mathematics, Vol. 119, No. 10c, 2018, pp. 553-562.
[15] A. Jebaraj Rathnakumar, S.Balakrishnan, “Machine Learning based Grape Leaf Disease Detectionâ€, Jour of Adv Research in Dynamical & Control Systems, Vol. 10, 08-Special Issue, 2018. Pp. 775-780.
[16] A. Jebaraj Ratnakumar, S. Balakrishnan, S.Sheeba Rani, V.Gomathi, “A Machine Learning Based IOT Device for E-Health Monitoring In a Cloud Environmentâ€, Invest Clin. Vol. 58, issue 3, pp. 287-299, 2017. (Web of Science).
[17] S. Vasu, A.K. Puneeth Kumar, T. Sujeeth, Dr.S. Balakrishnan, “A Machine Learning Based Approach for Computer Securityâ€, Jour of Adv Research in Dynamical & Control Systems. Vol.10, 11-Special issue, 2018, pp. 915- 919.
[18] Balakrishnan, S., Janet, J., Sujatha, K., & Rani, S. (2018). An Efficient and Complete Automatic System for Detecting Lung Module. Indian Journal Of Science And Technology, 11(26). doi:10.17485/ijst/2018/v11i26/130559
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How to Cite
Winston Paul, D., Balakrishnan, S., & Velusamy, A. (2018). Rule based Hybrid Weighted Fuzzy Classifier for Tumor Data. International Journal of Engineering & Technology, 7(4.19), 104-108. https://doi.org/10.14419/ijet.v7i4.19.22030Received date: 2018-11-28
Accepted date: 2018-11-28
Published date: 2018-11-27