Rule based Hybrid Weighted Fuzzy Classifier for Tumor Data

  • Authors

    • D. Winston Paul
    • S. Balakrishnan
    • A. Velusamy
    https://doi.org/10.14419/ijet.v7i4.19.22030

    Received date: November 28, 2018

    Accepted date: November 28, 2018

    Published date: November 27, 2018

  • 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

  • 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 Techniques”, 2nd edition, ISBN: 978-55860-901-3, Elsevier.
    6. Max Bramer, “Principles of Data Mining”, ISBN: 978-81-8489-166-9, springer, 2007.
    7. Tomoharu Nakashima, Yasuyuki Yokota, Hisao Ishibuchi, “Constructing fuzzy classification systems from weighted training patterns”, 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 Data”, 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 Learning”, 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 Detection”, 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 Environment”, 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 Security”, 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

  • Downloads

  • How to Cite

    Winston Paul, D., Balakrishnan, S., & Velusamy, A. (2018). Rule based Hybrid Weighted Fuzzy Classifier for Tumor Data. International Journal of Engineering and Technology, 7(4.19), 104-108. https://doi.org/10.14419/ijet.v7i4.19.22030