Improving Imputation Accuracy in Ordinal Data Using Classification
- 1.2k Downloads
Tackling missing data is one of the fundamental data pre-processing steps. Data analysis and pattern extraction are affected due to the underlying differences between instances with and without missing data. This is a particular problem with ordinal data, where for example a sample of a population may have all failed to answer a specific question in a questionnaire. The existing methods such as listwise deletion, mean attribute substitution, and regression substitution, naively impute data. They do not impute plausible values as they fail to take into account the relationships between the attributes, but instead consider the distribution of the attribute with missing values only. In this paper we introduce the use of Classification Based Imputation (CNI) to replace missing values with plausible values in ordinal data. The results show that not only does the CNI based technique outperform the existing approaches for imputing missing values in ordinal data but it also helps to improve the classification accuracy of machine learning algorithms.
- 4.De Leeuw, E.D., Hox, J., Huisman, M.: Prevention and treatment of item nonresponse. J. Official Stat.-Stockh. 19(2), 153–176 (2003)Google Scholar
- 5.Finch, W.H.: Imputation methods for missing categorical questionnaire data: a comparison of approaches. J. Data Sci. 8(3), 361–378 (2010)Google Scholar
- 6.Su, X., Greiner, R., Khoshgoftaar, T.M., Napolitano, A.: Using classifier-based nominal imputation to improve machine learning. In: Huang, J.Z., Cao, L., Srivastava, J. (eds.) PAKDD 2011. LNCS (LNAI), vol. 6634, pp. 124–135. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-20841-6_11 CrossRefGoogle Scholar
- 11.Su, X., Khoshgoftaar, T.M., Greiner, R.: Using imputation techniques to help learn accurate classifiers. In: 20th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2008, vol. 1, pp. 437–444. IEEE (2008)Google Scholar