International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies

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:: International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies

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ISSN 2228-9860
eISSN 1906-9642
CODEN: ITJEA8


FEATURE PEER-REVIEWED ARTICLE

Vol.12(6) (2021)

References

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  2. Bonilla-Huerta, E., Hernandez-Montiel, A., Morales-Caporal, R., & Arjona-Lopez, M. (2015). Hybrid framework using multiple filters and an embedded approach for an efficient selection and classification of microarray data. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 13(1), 12-26.
  3. Brankovic, A., Hosseini, M., & Piroddi, L. (2018). A distributed feature selection algorithm based on distance correlation with an application to microarrays. IEEE/ACM transactions on computational biology and bioinformatics, 16(6), 1802-1815.
  4. Cao, B., Zhao, J., Yang, P., Yang, P., Liu, X., Qi, J., & Muhammad, K. (2019). Multiobjective feature selection for microarray data via distributed parallel algorithms. Future Generation Computer Systems, 100, 952-981.
  5. Dancey, J.E, Bedard, P.L, Onetto, N& Hudson, T.J (2012). The genetic basis for cancer treatment decisions. Cell, 148(3), 409-420.
  6. Dash, R. (2020). A two-stage grading approach for feature selection and classification of microarray data using Pareto-based feature ranking techniques: A case study. Journal of King Saud University-Computer and Information Sciences, 32(2), 232-247.
  7. Ebrahimpour, M. K., Nezamabadi-Pour, H., & Eftekhari, M. (2018). CCFS: A cooperating coevolution technique for large-scale feature selection on microarray datasets. Computational biology and chemistry, 73, 171-178.
  8. Kang, C., Huo, Y., Xin, L., Tian, B., & Yu, B. (2019). Feature selection and tumor classification for microarray data using relaxed Lasso and generalized multi-class support vector machine. Journal of theoretical biology, 463, 77-91.
  9. Ke, W., Wu, C., Wu, Y., & Xiong, N. N. (2018). A new filter feature selection based on criteria fusion for gene microarray data. IEEE Access, 6, 61065-61076.
  10. Kumar, M., Rath, N. K., Swain, A., &Rath, S. K. (2015). Feature selection and classification of microarray data using MapReduce based ANOVA and K-Nearest neighbor. Procedia Computer Science, 54, 301-310.
  11. Kuo, W. P, Kim, E. Y, Trimarchi, J, Jenssen, T. K, Vinterbo, S. A& Ohno-Machado, L (2004). A primer on gene expression and microarrays for machine learning researchers. Journal of Biomedical Informatics, 37(4), 293-303.
  12. Mazumder, D. H., & Veilumuthu, R. (2019). An enhanced feature selection filter for classification of microarray cancer data. ETRI Journal, 41(3), 358-370.
  13. Mollaee, M., & Moattar, M. H. (2016). A novel feature extraction approach based on ensemble feature selection and modified discriminant independent component analysis for microarray data classification. Biocybernetics and Biomedical Engineering, 36(3), 521-529.
  14. Peng, Y., Wu, Z., & Jiang, J. (2010). A novel feature selection approach for biomedical data classification. Journal of Biomedical Informatics, 43(1), 15-23.
  15. Sathya, M. & Manju Priya, S. (2019). PSO search-based feature selection method for high dimensional data. International Journal of Recent Technology & Engineering, 7(583), 485-488.
  16. Sathya, M. & Manju Priya, S. (2020). Modified whale optimization algorithm for feature selection algorithm in microarray cancer datasets. International Journal of Scientific & Technology Research, 9(3), 549-556.
  17. Seijo-Pardo, B., Bolon-Canedo, V., & Alonso-Betanzos, A. (2016, April). Using a feature selection ensemble on DNA microarray datasets. In ESANN.
  18. Sun, S., Peng, Q., & Zhang, X. (2016). Global feature selection from microarray data using Lagrange multipliers. Knowledge-Based Systems, 110, 267-274.
  19. Tang, J., & Zhou, S. (2016). A new approach for feature selection from microarray data based on mutual information. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 13(6), 1004-1015.
  20. Wang, H., Jing, X., & Niu, B. (2017). A discrete bacterial algorithm for feature selection in classification of microarray gene expression cancer data. Knowledge-Based Systems, 126, 8-19.
  21. Wang, H., Tan, L., & Niu, B. (2019). Feature selection for classification of microarray gene expression cancers using bacterial colony optimization with multi-dimensional population. Swarm and Evolutionary Computation, 48, 172-181.
  22. Yan, C., Ma, J., Luo, H., & Patel, A. (2019). Hybrid binary coral reefs optimization algorithm with simulated annealing for feature selection in high-dimensional biomedical datasets. Chemometrics and Intelligent Laboratory Systems, 184, 102-111.
  23. Zare, M., Eftekhari, M., & Aghamollaei, G. (2019). Supervised feature selection via matrix factorization based on singular value decomposition. Chemometrics and Intelligent Laboratory Systems, 185, 105-113.


Other issues:
Vol.12(5)(2021)
Vol.12(4)(2021)
Vol.12(3)(2021)
Vol.12(2)(2021)
Vol.12(1)(2021)
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