726
Views
5
CrossRef citations to date
0
Altmetric
Articles

Improving Naive Bayes for Regression with Optimized Artificial Surrogate Data

&

References

  • Agrawal, R., T. Imielinski, and A. Swami. 1993. Database mining: A performance perspective. IEEE Transactions on Knowledge and Data Engineering 5 (6):914–25. doi:10.1109/TKDE.69.
  • Bonyadi, M., and Z. Michalewicz. 2017. Particle swarm optimization for single objective continuous space problems: A review. Evolutionary Computation 25 (1):1–54. doi:10.1162/EVCO_r_00180.
  • Brameier, M., and W. Banzhaf. 2007. Linear genetic programming. Springer US.
  • Breiman, L. 1996. Bagging predictors. Machine Learning 24 (2):123–40. doi:10.1007/BF00058655.
  • Breiman, L. 2001. Random forests. Machine Learning 45 (1):5–32. doi:10.1023/A:1010933404324.
  • Candanedo, L., V. Feldheim, and D. Deramix. 2017. Data driven prediction models of energy use of applicances in a low energy house. Energy and Buildings 140:81–97. doi:10.1016/j.enbuild.2017.01.083.
  • Chawla, N., K. Bowyer, L. Hall, and W. Kegelmeyer. 2002. Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research 16:321–57. doi:10.1613/jair.953.
  • Cheng, R., and Y. Jin. 2015. A competitive swarm optimizer for large scale optimization. IEEE Transactions on Cybernetics 45 (2):191–204. doi:10.1109/TCYB.2014.2322602.
  • Cortez, P., A. Cerdeira, F. Almeida, T. Matos, and J. Reis. 2009. Modeling wine preferences by data mining from physicochemical properties. Decision Support Systems 47 (4):547–53. doi:10.1016/j.dss.2009.05.016.
  • Escalante, H., K. Mendoza, M. Graff, and A. Morales-Reyes. 2013. Genetic programming of prototypes for pattern classification, 100–07. Berlin, Heidelberg: Springer Berlin Heidelberg.
  • Frank, E., M. Hall, and I. Witten. 2016. The WEKA workbench. Online Appendix for “Data Mining: Practical Machine Learning Tools and Techniques”. Morgan Kaufmann US, 4th edition.
  • Frank, E., L. Trigg, G. Holmes, and I. Witten. 2000. Naive Bayes for regression. Machine Learning 41:5–25. doi:10.1023/A:1007670802811.
  • Goodfellow, I., J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio. 2014. Generative adversarial nets. In Ghahramani Z. et al. (Eds.) Advances in Neural Information Processing (NIPS) 27, pp. 2672-2680, Current Associates Inc.
  • Greene, C., D. Hill, and J. Moore. 2009. Environmental noise improves epistasis models of genetic data discovered using a computational evolution system. Proc. GECCO’09, 1785–86, Montreal, Canada.
  • Guozhong, A. 1996. The effects of adding noise during backpropagation training on generalisation perfor- mance. Neural Computation 8 (3):643–74. doi:10.1162/neco.1996.8.3.643.
  • Impedovo, S., F. Mangini, and D. Barbuzzi. 2014. A novel prototype generation technique for handwriting digit recognition. Pattern Recognition 47 (3):1002–10. doi:10.1016/j.patcog.2013.04.016.
  • Kapur, A., K. Marwah, and G. Alterovitz. 2016. Gene expression prediction using low-rank matrix completion. BMC Bioinformatics 17:243. doi:10.1186/s12859-016-1106-6.
  • Kennedy, J., R. Eberhart, and Y. Shi. 2001. Swarm intelligence. The Morgan Kaufmann Series in Artificial Intelligence. Morgan Kauffman, US.
  • Koh, P. W., and P. Liang. 2017. Understanding black-box predictions via influence functions. In Proceedings of the 34th International Conference on Machine Learning, volume 70 of Proceedings of Machine Learning Research, ed. D. Precup and Y. W. Teh, 1885–94. Sydney, Australia: International Convention Centre, August 06–11. PMLR. http://proceedings.mlr.press/v70/koh17a.html.
  • Leisch, F., and E. Dimitriadou. 2010. MLbench: Machine learning benchmark problems, v. 2.1-1 edition.
  • Lenz, G., G. Wright, S. S. Dave, W. Xiao, J. Powell, H. Zhao, W. Xu, B. Tan, N. Goldschmidt, J. Iqbal, et al. 2008. Stromal gene signatures in large-b-cell lymphomas. New England Journal of Medicine 359 (22):2313–23. PMID: 19038878. doi:10.1056/NEJMoa0802885.
  • Lòpez, V., I. Triguero, C. Carmona, S. Garcia, and F. Herrara. 2014. Addressing imbalanced classification with instance generation techniques: IPADE-ID. Neurocomputing 126 (15–28):15–28. doi:10.1016/j.neucom.2013.01.050.
  • Mayo, M., and Q. Sun. 2014. Evolving artificial datasets to improve interpretable classifiers. 2014 IEEE Congress on Evolutionary Computation (CEC), Beijing, China, 2367–74.
  • Triguero, I., J. Derrac, S. Garcia, and F. Herrera. 2011. A taxonomy and experimental study on prototype generation for nearest neighbor classification. IEEE Trans. On Systems, Man and Cybernetics – Part C: Applications and Reviews 42 (1):86–100. doi:10.1109/TSMCC.2010.2103939.
  • Tsanas, A., M. A. Little, P. E. McSharry, and L. O. Ramig. 2010. Accurate telemonitoring of Parkinson’s disease progression by non-invasive speech tests. IEEE Transactions on Biomedical Engineering 57:884–93. doi:10.1109/TBME.2009.2036000.
  • Tsanas, A., and A. Xifara. 2012. Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools. Energy and Buildings 49:560–67. doi:10.1016/j.enbuild.2012.03.003.
  • Urbanowicz, R., N. Sinnot-Armstrong, and J. Moore. 2011. Random artificial incorporation of noise in a learning classifier system environment. Proc. GECCO’11, 369–74, Helsinki, Finland.
  • Vincent, P., H. Larochelle, Y. Bengio, and P. Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. Proc. 25th International Conference on Machine Learning (ICML’08), Helsinki, Finland.
  • Wang, Y., and I. Witten. 1997. Induction of model trees for predicting continuous classes. In Poster papers of the 9th European Conference on Machine Learning, Springer, Prague, Czech Republic
  • Wang, Z. bujar: Buckly-James regression for survival data with high dimensional covariates. Technical report, 2015. doi:10.1094/PDIS-09-14-0954-PDN.
  • Zhong, K., R. Guo, S. Kumar, B. Yan, D. Simcha, and I. Dhillon. 2017. Fast classification with binary prototypes. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, vol 54, 1255–63, Florida, US. PMLR.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.