Missing and Incomplete Data Handling in Cybersecurity Applications

1. Andridge, R.R., Little, R.J.A.: A review of hot deck imputation for survey non-response. Int. Stat. Rev. 78(1), 40–64 (2010)

   Article  Google Scholar 

2. Azur, M.J., Stuart, E.A., Frangakis, C., Leaf, P.J.: Multiple imputation by chained equations: what is it and how does it work? Int. J. Meth. Psychiatr. Res. 20(1), 40–49 (2011)

   Article  Google Scholar 

3. Baguley, T., Andrews, M.: Handling missing data. In: Modern Statistical Methods for HCI, pp. 57–82 (2016)

   Google Scholar 

4. Baio, Gianluca, Leurent, Baptiste: An introduction to handling missing data in health economic evaluations. In: Round, Jeff (ed.) Care at the End of Life, pp. 73–85. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-28267-1_6

   Chapter  Google Scholar 

5. Benferhat, S, Tabia, K., Ali, M.: Advances in artificial intelligence: from theory to practice. In: 30th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE, pages Proceedings, Part I (2017)

   Google Scholar 

6. Choraś, M., Pawlicki, M.: Intrusion detection approach based on optimised artificial neural network. Neurocomputing (2020)

   Google Scholar 

7. Doreswamy, I.G., Manjunatha, B.R.: Performance evaluation of predictive models for missing data imputation in weather data. In: 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 1327–1334 (2017)

   Google Scholar 

8. Ezzine, I., Benhlima, L.: A study of handling missing data methods for big data. In: 2018 IEEE 5th International Congress on Information Science and Technology (CiSt), pp. 498–501 (2018)

   Google Scholar 

9. Fan, W., Geerts, F.: Foundations of data quality management. Synth. Lect. Data Manage. 4(5), 1–217 (2012)

   Article  Google Scholar 

10. Chang, G., Ge, T.: Comparison of missing data imputation methods for traffic flow. In: Proceedings 2011 International Conference on Transportation, Mechanical, and Electrical Engineering (TMEE), pp. 639–642 (2011)

    Google Scholar 

11. Gleason, T.C., Staelin, R.: A proposal for handling missing data. Psychometrika 40(2), 229–252 (1975)

    Article  Google Scholar 

12. Graham, J.W.: Missing Data. Springer, New York, New York, NY (2012)

    Book  Google Scholar 

13. Jakobsen, J.C., Gluud, C., Wetterslev, J., Winkel, P.: When and how should multiple imputation be used for handling missing data in randomised clinical trials - a practical guide with flowcharts. BMC Med. Res. Meth. 17(1), 162 (2017)

    Article  Google Scholar 

14. Ksieniewicz, P., Woźniak, M.: Imbalanced data classification based on feature selection techniques. In: International Conference on Intelligent Data Engineering and Automated Learning, pp. 296–303. Springer (2018)

    Google Scholar 

15. Li, Q., Tan, H., Wu, Y., Ye, L., Ding, F.: Traffic flow prediction with missing data imputed by tensor completion methods. IEEE Access 8, 63188–63201 (2020)

    Article  Google Scholar 

16. Liu, S., Dai, H.: Examination of reliability of missing value recovery in data mining. In: 2014 IEEE International Conference on Data Mining Workshop, pp. 306–313 (2014)

    Google Scholar 

17. Lu, X., Si, J., Pan, L., Zhao, Y.: Imputation of missing data using ensemble algorithms. In: 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), vol. 2, pp. 1312–1315 (2011)

    Google Scholar 

18. Mazumder, R., Hastie, T., Tibshirani, R.: Spectral regularization algorithms for learning large incomplete matrices. J. Mach. Learn. Res. 11(80), 2287–2322 (2010)

    MathSciNet  MATH  Google Scholar 

19. Nogueira, B.M., Santos, T.R.A., Zarate, L.E.: Comparison of classifiers efficiency on missing values recovering: application in a marketing database with massive missing data. In: 2007 IEEE Symposium on Computational Intelligence and Data Mining, pp. 66–72 (2007)

    Google Scholar 

20. Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)

    MathSciNet  MATH  Google Scholar 

21. Prince, J., Andreotti, F., De Vos, M.: Evaluation of source-wise missing data techniques for the prediction of parkinson’s disease using smartphones. In: ICASSP 2019–2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3927–3930 (2019)

    Google Scholar 

22. Raghunathan, T.E., Lepkowski, J.M., Hoewyk, J.V., Solenberger, P.: A multivariate technique for multiply imputing missing values using a sequence of regression models. Surv. Methodol. 27(1), 85–96 (2001)

    Google Scholar 

23. Rana, S., John, A.H., Midi, H.: Robust regression imputation for analyzing missing data. In: 2012 International Conference on Statistics in Science, Business and Engineering (ICSSBE), pp. 1–4 (2012)

    Google Scholar 

24. Rubin, D.B.: Inference and missing data. Biometrika 63(3), 581–592 (1976)

    Article  MathSciNet  Google Scholar 

25. Rubinsteyn, A., Feldman, S., O’Donnell, T., Beaulieu-Jones, B.: Hammerlab/fancyimpute: Version 0.2. 0. Zenodo. doi, 10 (2017)

    Google Scholar 

26. Sakurai, D., et al.: Estimation of missing data of showcase using artificial neural networks. In: 2017 IEEE 10th International Workshop on Computational Intelligence and Applications (IWCIA), pp. 15–18 (2017)

    Google Scholar 

27. Santos, M.S., Pereira, R.C., Costa, A.F., Soares, J.P., Santos, J., Abreu, P.H.: Generating synthetic missing data: a review by missing mechanism. IEEE Access 7, 11651–11667 (2019)

    Article  Google Scholar 

28. Sessa, J., Syed, D.: Techniques to deal with missing data. In: 2016 5th International Conference on Electronic Devices, Systems and Applications (ICEDSA), pp. 1–4 (2016)

    Google Scholar 

29. Sharafaldin, I., Lashkari, A.H., Ghorbani, A.A.: Toward generating a new intrusion detection dataset and intrusion traffic characterization. In: ICISSP, pp. 108–116 (2018)

    Google Scholar 

30. Shi, W., et al.: Effective prediction of missing data on apache spark over multivariable time series. IEEE Trans. Big Data 4(4), 473–486 (2018)

    Article  Google Scholar 

31. Stekhoven, D.J., Buhlmann, P.: MissForest-non-parametric missing value imputation for mixed-type data. Bioinformatics 28(1), 112–118 (2012)

    Article  Google Scholar 

32. Tripathi, A.K., Rathee, G., Saini, H.: Taxonomy of missing data along with their handling methods. In: 2019 Fifth International Conference on Image Information Processing (ICIIP), pp. 463–468 (2019)

    Google Scholar 

33. Troyanskaya, O., et al.: Missing value estimation methods for DNA microarrays. Bioinformatics 17(6), 520–525 (2001)

    Article  Google Scholar 

34. Umathe, V.H., Chaudhary, G.: Imputation methods for incomplete data. In: 2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), pp. 1–4 (2015)

    Google Scholar 

35. Wang, H., Shouhong.: A knowledge acquisition method for missing data. In: 2008 International Symposium on Knowledge Acquisition and Modeling, pp. 152–156 (2008)

    Google Scholar 

36. Yeon, H., Son, H., Jang, Y.: Visual imputation analytics for missing time-series data in Bayesian network. In: 2020 IEEE International Conference on Big Data and Smart Computing (BigComp), pp. 303–310 (2020)

    Google Scholar 

37. Zeng, D., Xie, D., Liu, R., Li, X.: Missing value imputation methods for TCM medical data and its effect in the classifier accuracy. In: 2017 IEEE 19th International Conference on e-Health Networking, Applications and Services (Healthcom), pp. 1–4 (2017)

    Google Scholar 

38. Zhang, L., Xie, Y., Xi-dao, L., Zhang, X.: Multi-source heterogeneous data fusion. In: 2018 International Conference on Artificial Intelligence and Big Data (ICAIBD), pp. 47–51 (2018)

    Google Scholar 

39. Zhang, Y., Kambhampati, C., Davis, D.N., Goode, K., Cleland, J.G F.: A comparative study of missing value imputation with multiclass classification for clinical heart failure data. In: 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery, pp. 2840–2844 (2012)

    Google Scholar 

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