528
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Analysis of Proposed and Traditional Boosting Algorithm with Standalone Classification Methods for Classifying Gene Expresssion Microarray Data Using a Reject Option

, , , , , , & show all
Article: 2151171 | Received 26 Dec 2021, Accepted 18 Nov 2022, Published online: 30 Nov 2022

References

  • Alon, U., N. Barkai, D. A. Notterman, K. Gish, S. Ybarra, D. Mack, and A. J. Levine (1999). Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proceedings of the National Academy of Sciences, 96(12), 6745–3907.
  • Bartlett, P., Y. Freund, W. S. Lee, and R. E. Schapire. 1998. Boosting the margin: A new explanation for the effectiveness of voting methods. Annals of Statistics 26(5):1651–86. doi:10.1214/aos/1024691352.
  • Bates, D., M. Cohen, M. Leape, L. Overhage, J. M. Shabot, and M. M. Sheridan. 2001. Reducing the frequency of errors in medicine using information technology. Journal of the American Medical Informatics Association 8(4):299–308. doi:10.1136/jamia.2001.0080299.
  • Breiman, L. 2001. Random forests. Machine Learning 45(1):5–32. doi:10.1023/A:1010933404324.
  • Cover, T. M., and P. E. Hart. 1967. Nearest neighbor pattern classification. IEEE Transactions on Information Theory 13(1):21–27. doi:10.1109/TIT.1967.1053964.
  • Freund, Y., and R. E. Schapire. 1997. A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. Journal of Computer and System Sciences 55(1):119–39. doi:10.1006/jcss.1997.1504.
  • Golub, T. R., D. K. Slonim, C. H. P. Tamayo, J. P. Gaasenbeek, M. Mesirov, M. L. Coller, H. Loh, J. R. Downing, M. A. Caligiuri, and C. D. Bloomfield. 1999. Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science 286(5439):531–37. doi:10.1126/science.286.5439.531.
  • Hanczar, B., and E. R. Dougherty. 2008. Classification with reject option in gene expression data. Bioinformatics 24(17):1889–95. doi:10.1093/bioinformatics/btn349.
  • Kotsiantis, S. B., I. Zaharakis, and P. Pintelas. 2007. Supervised machine learning: A review of classification techniques. Emerging artificial intelligence applications in computer engineering 160(1): 3–24.
  • Man, M. Z., G. Dyson, K. Johnson, and B. Liao. 2004. Evaluating methods for classifying expression data. Journal of Biopharmaceutical Statistics 14(4):1065–84. doi:10.1081/BIP-200035491.
  • Park, C., and H. Park. 2005. A relationship betweenLDA and the generalized minimum squared error solution. SIAM Journal on Matrix Analysis and Applications 27(2):474–92. doi:10.1137/040607599.
  • Shipp, M. A., K. N. Ross, P. Tamayo, A. P. Weng, J. L. Kutok, R. C. Aguiar, and T. R. Golub. 2002. Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nature medicine 8 (1):68–74.
  • Vapnik, V. N. 1999. An overview of statistical learning theory. IEEE Transactions on Neural Networks 10(5):988–99. doi:10.1109/72.788640.
  • Veer, L. J., H. Dai, M. J. Vijver, Y. D. He YD, A. A. M. Hart, M. Mao, H. L. Peterse, K. Kooy, M. J. Marton, A. T. Witteveen at, et al. 2002. Gene expression pro?ling predicts clinical outcome of breast cancer. Nature 415(6871):530–36. doi:10.1038/415530a.
  • Wiering, M., and H. H. van.2007. Two novel on-policy reinforcement learning algorithms based on TD(λ)-methods. in Proceedings of the IEEE International Symposium on Adaptive Dynamic Programming and Reinforcement Learning. Honolulu, HI, USA. 280–87.
  • Yu, F., T. K. Houston, M. N. Ray, D. Q. Garner, and E. S. Berner. 2007. Clinical decision support systems: State of the Art. Medical Decision Making 27(6):744–53. No. 09-0069-EF. Med Decis Making. doi:10.1177/0272989X07305321.
  • Zhou, X., and K. Z. Mao. 2005. LS Bound Based Gene Selection for DN Microarray Data. Bioinformatics 21(8):1559–64. doi:10.1093/bioinformatics/bti216.