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Original Articles

Missing Data Imputation for Supervised Learning

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References

  • Batista, G. E. A. P. A., and M. C. Monard. 2003. An analysis of four missing data treatment methods for supervised learning. Applied Artificial Intelligence 17 (5–6):519–33. doi:10.1080/713827181.
  • Beck, N., G. King, and L. Zeng. 2000. Improving quantitative studies of international conflict: A conjecture. American Political Science Review 94 (1):21–35. doi:10.1017/S0003055400220078.
  • Cant, F., and S. M. Saiegh. 2011. Fraudulent democracy? An analysis of Argentina’s infamous decade using supervised machine learning. Political Analysis 19 (4):409–33. doi:10.1093/pan/mpr033.
  • de Leeuw, E. D., J. Hox, M. Huisman, et al. 2003. Prevention and treatment of item nonresponse. Journal of Official Statistics 19 (2):153–76.
  • Heskes, T. 1997. Practical confidence and prediction intervals. In Advances in neural information processing systems, Vol. 9, ed. M. Mozer, et al., 176–82. Cambridge, MA: MIT Press.
  • Hill, D. W., and Z. M. Jones. 2014. An empirical evaluation of explanations for state repression. American Political Science Review 108 (3):661–87. doi:10.1017/S0003055414000306.
  • Hinton, G. E., N. Srivastava, A. Krizhevsky, I. Sutskever, and R. R. Salakhutdinov. 2012. Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580.
  • Jones, M. P. 1996. Indicator and stratification methods for missing explanatory variables in multiple linear regression. Journal of the American Statistical Association 91 (433):222–30. doi:10.1080/01621459.1996.10476680.
  • Kohavi, R. 1996. Scaling up the accuracy of naive-Bayes classifiers: A decision-tree hybrid. In Proceedings of the second international conference on knowledge discovery and data mining, ed. E. Simoudis, et al., 202–07. Menlo Park, California: AAAI Press.
  • Li, D., J. Deogun, W. Spaulding, and B. Shuart. 2004. Towards missing data imputation: A study of fuzzy k-means clustering method. In Rough sets and current trends in computing, ed. S. Tsumoto, et al., 573–79. Berlin, Heidelberg, Germany: Springer.
  • Lichman, M. 2013. “UCI Machine Learning Repository.” Accessed October 8, 2015. http://archive.ics.uci.edu/ml.
  • Little, R. J. A., and D. B. Rubin. 2014. Statistical Analysis with Missing Data. Hoboken, NJ: John Wiley & Sons.
  • Maaten, L., M. Chen, S. Tyree, and K. Q. Weinberger. 2013. Learning with marginalized corrupted features. In Proceedings of the 30th international conference on machine learning, ed. S. Dasgupta and D. McAllester, 410–18. Atlanta, GA: PMLR.
  • Montgomery, J. M., S. Olivella, J. D. Potter, and B. F. Crisp. 2015. An informed forensics approach to detecting vote irregularities. Political Analysis 23 (4):488–505. doi:10.1093/pan/mpv023.
  • Muchlinski, D., D. Siroky, H. Jingrui, and M. Kocher. 2016. Comparing random forest with logistic regression for predicting class-imbalanced civil war onset data. Political Analysis 24 (1):87–103. doi:10.1093/pan/mpv024.
  • Schlimmer, J. C. 1987. “Concept Acquisition Through Representational Adjustment.” PhD diss., Department of Information and Computer Science, University of California, Irvine.
  • Schlimmer, J. C., and R. H. Granger Jr. 1986. Incremental learning from noisy data. Machine Learning 1 (3):317–54. doi:10.1007/BF00116895.
  • Silva-Ramrez, E.-L., R. Pino-Mejas, M. Lpez-Coello, and C.-D.-L.-V. Mara-Dolores. 2011. Missing value imputation on missing completely at random data using multilayer perceptrons. Neural Networks 24 (1):121–29. doi:10.1016/j.neunet.2010.09.008.
  • Snoek, J., H. Larochelle, and R. P. Adams. 2012. Practical Bayesian optimization of machine learning algorithms. In Advances in neural information processing systems 25, ed. F. Pereira, et al., 2951–59. Red Hook, NY: Curran Associates, Inc.
  • Tsiatis, A. 2007. Semiparametric Theory and Missing Data. New York, NY: Springer Science & Business Media.
  • Wager, S., S. Wang, and P. S. Liang. 2013. Dropout training as adaptive regularization. In Advances in neural information processing systems, 26, ed. C. J. C. Burges, et al., 351–59. Red Hook, NY: Curran Associates, Inc.
  • Wang, S., and C. Manning. 2013. Fast dropout training. In Proceedings of the 30th international conference on machine learning, ed. S. Dasgupta and D. McAllester, 118–26. Atlanta, GA: PMLR.
  • Zeiler, M. D. 2012. ADADELTA: An adaptive learning rate method. arXiv preprint arXiv:1212.5701.

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