Missing Features Reconstruction and Its Impact on Classification Accuracy
In real-world applications, we can encounter situations when a well-trained model has to be used to predict from a damaged dataset. The damage caused by missing or corrupted values can be either on the level of individual instances or on the level of entire features. Both situations have a negative impact on the usability of the model on such a dataset. This paper focuses on the scenario where entire features are missing which can be understood as a specific case of transfer learning. Our aim is to experimentally research the influence of various imputation methods on the performance of several classification models. The imputation impact is researched on a combination of traditional methods such as k-NN, linear regression, and MICE compared to modern imputation methods such as multi-layer perceptron (MLP) and gradient boosted trees (XGBT). For linear regression, MLP, and XGBT we also propose two approaches to using them for multiple features imputation. The experiments were performed on both real world and artificial datasets with continuous features where different numbers of features, varying from one feature to \(50\%\), were missing. The results show that MICE and linear regression are generally good imputers regardless of the conditions. On the other hand, the performance of MLP and XGBT is strongly dataset dependent. Their performance is the best in some cases, but more often they perform worse than MICE or linear regression.
KeywordsMissing features Imputation methods Feature reconstruction Transfer learning
This research was supported by SGS grant No. SGS17/210/OHK3/3T/18 and by GACR grant No. GA18-18080S.
- 1.Alcalá-Fdez, J., et al.: KEEL data-mining software tool: data set repository, integration of algorithms and experimental analysis framework. J. Mult.-Valued Log. Soft Comput. 17, 255–287 (2011)Google Scholar
- 5.Baitharu, T.R., Pani, S.K.: Effect of missing values on data classification. J. Emerg. Trends Eng. Appl. Sci. (JETEAS) 4(2), 311–316 (2013)Google Scholar
- 6.Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016, pp. 785–794. ACM, New York (2016)Google Scholar
- 9.Jonsson, P., Wohlin, C.: An evaluation of k-nearest neighbour imputation using Likert data. In: Proceedings of the 10th International Symposium on Software Metrics, pp. 108–118, September 2004Google Scholar
- 13.Lichman, M.: UCI machine learning repository (2013)Google Scholar
- 25.Zhang, Q., Rahman, A., D’este, C.: Impute vs. ignore: missing values for prediction. In: The 2013 International Joint Conference on Neural Networks (IJCNN), pp. 1–8, August 2013Google Scholar
- 26.Zhu, M., Cheng, X.: Iterative KNN imputation based on GRA for missing values in TPLMS. In: 2015 4th International Conference on Computer Science and Network Technology (ICCSNT), vol. 1, pp. 94–99. IEEE (2015)Google Scholar