ICTACT Journal on Soft Computing

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A Hybrid Ensemble Method for Accurate Fuzzy and Support Vector Machine for Gene Expression in Data Mining


Affiliations
1 Department of Computer Science, KSG College of Arts and Science, India
     

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Malignancy is a bunch of infection which spreads all through the human body. Since it is an exceptionally deceptive illness its determination is of vital importance. Information mining innovation helps in arranging and bunching the malignancy information and this procedure assists with distinguishing potential disease patients by investigating the data alone. In this examination we analyze three information mining calculations, namely PCA, Genetic calculation and Hierarchical Fuzzy C Means (HFCM). The hereditary calculation is done using the Quantum-enhanced Support Vector Machine (QSVM). The outcome demonstrates that the proposed calculation accomplishes a better outcome when contrasted to the other two calculations.

Keywords

PCA, Genetic Algorithm, Hierarchical Fuzzy C Mean, QSVMs, Cluster.
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  • Y. Wang and F. Makedon, “Application of Relief-F Feature Filtering Algorithm to Selecting Informative Genes for Cancer Classification using Microarray Data”, Proceedings of IEEE International Conference on Computational Systems Bioinformatics, pp. 497-498, 2004.

  • C. Saravanabhavan, T. Saravanan, D.B. Mariappan and K.M. Baalamurugan, “Data Mining Model for Chronic Kidney Risks Prediction Based on using NB-CbH”, Proceedings of International Conference on Advance Computing and Innovative Technologies in Engineering, pp. 1023-1026, 2021.

  • D. Huang, Y. Quan, M. He and B. Zhou, “Comparison of Linear Discriminant Analysis methods for the Classification of Cancer based on Gene Expression Data”, Journal of Experimental and Clinical Cancer Research, Vol. 28, No. 1, pp.1-8, 2009.

  • Y. Han and L. Yu, “Margin based Sample Weighting for Stable Feature Selection”, Proceedings of International Conference on Web-Age Information Management, pp. 680-691, 2010.

  • V. Chang, B. Gobinathan, A. Pinagapani and S. Kannan, “Automatic Detection of Cyberbullying using Multi-Feature based Artificial Intelligence with Deep Decision Tree Classification”, Computers and Electrical Engineering, Vol. 92, pp. 1-21, 2021.

  • P. Valarmathie, T. Ravichandran and Dinakaran, K, “Survey on Clustering Algorithms for Microarray Gene Expression Data”, European Journal of Scientific Research, Vol. 69, No. 1, pp. 5-20, 2012.

  • S. Hengpraprohm and P. Chongstitvatana, “Feature Selection by Weighted-SNR for Cancer Microarray Data Classification”, International Journal of Innovative Computing, Information and Control, Vol. 5, No. 12, pp. 4627-4636, 2009.

  • N. Singh, K. Guliani and P. Prabhat, “Comparison of Different Filtering Approaches on Gene Expression Data for Clustering”, International Journal of Engineering Research and Technology, Vol. 2, No. 5, pp. 815-818, 2013.

  • R. Anand, S. Ravichandran and S. Chatterjee, “A New Method of Finding Groups of Coexpressed Genes and Conditions of Coexpression”, BMC Bioinformatics, Vol. 17, No. 1, pp. 1-14, 2016.

  • J. Oyelade, I. Isewon, F. Oladipupo, O. Aromolaran, E. Uwoghiren, F. Ameh, M. Achas and E. Adebiyi, “Clustering Algorithms: their Application to Gene Expression Data”, Bioinformatics and Biology Insights, Vol. 10, pp. 237-253, 2016.

  • H.W. Nies, K.M. Daud, M.A. Remli, M.S. Mohamad, S. Deris, S. Omatu, S. Kasim and G. Sulong, “Classification

  • of Colorectal Cancer using Clustering and Feature Selection Approaches”, Proceedings of International Conference on Practical Applications of Computational Biology and Bioinformatics, pp. 58-65. 2017.

  • M.A. Remli, K.M. Daud, H.W. Nies, M.S. Mohamad, S. Deris, S. Omatu, S. Kasim and G. Sulong, “K-Means Clustering with Infinite Feature Selection for Classification Tasks in Gene Expression Data”, Proceedings of International Conference on Practical Applications of Computational Biology and Bioinformatics, pp. 50-57, 2017.

  • X. Yu, G. Yu and J. Wang, “Clustering Cancer Gene Expression Data by Projective Clustering Ensemble”, PloS One, Vol. 12, No. 2, pp. 1-21, 2017.

  • S.M. Ayyad, A.I. Saleh and L.M. Labib, “Gene Expression Cancer Classification using Modified K-Nearest Neighbors Technique”, Biosystems, Vol. 8, No. 2, pp. 41-51, 2019.

  • N. Naicker, T. Adeliyi and J. Wing, “Linear Support Vector Machines for Prediction of Student Performance in School-Based Education”, Mathematical Problems in Engineering, Vol. 2020, pp. 1-7, 2020.


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  • A Hybrid Ensemble Method for Accurate Fuzzy and Support Vector Machine for Gene Expression in Data Mining

Abstract Views: 354  |  PDF Views: 1

Authors

S. Vasanthakumar
Department of Computer Science, KSG College of Arts and Science, India
N. Ranjith
Department of Computer Science, KSG College of Arts and Science, India

Abstract


Malignancy is a bunch of infection which spreads all through the human body. Since it is an exceptionally deceptive illness its determination is of vital importance. Information mining innovation helps in arranging and bunching the malignancy information and this procedure assists with distinguishing potential disease patients by investigating the data alone. In this examination we analyze three information mining calculations, namely PCA, Genetic calculation and Hierarchical Fuzzy C Means (HFCM). The hereditary calculation is done using the Quantum-enhanced Support Vector Machine (QSVM). The outcome demonstrates that the proposed calculation accomplishes a better outcome when contrasted to the other two calculations.

Keywords


PCA, Genetic Algorithm, Hierarchical Fuzzy C Mean, QSVMs, Cluster.

References