A Quantitative Study of the Effect of Missing Data in Classifiers

Authors:
Peng Liu;Lei Lei;Naijun Wu
Affiliations:
Shanghai University of Finance and Economics;Shanghai University of Finance and Economics;Shanghai University of Finance and Economics
Venue:
CIT '05 Proceedings of the The Fifth International Conference on Computer and Information Technology
Year:
2005

Citing 0
Cited 3

Evidence and scenario sensitivities in naive Bayesian classifiers

International Journal of Approximate Reasoning
Rough neuro-fuzzy structures for classification with missing data

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
LAD-CBM; new data processing tool for diagnosis and prognosis in condition-based maintenance

Journal of Intelligent Manufacturing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In data mining approaches, predictive classification has a wide range of application. However, there are always missing data in the datasets, which affect the accuracy of classifiers. This paper will investigate the influence of missing data to classifier. The sensitivity analysis of six classifiers to missing data is studied in experiments. The results indicate that, in the datasets, when the proportion of missing data exceeds 20%, they do have a huge adverse effect on the prediction accuracy. Among the six classifiers, the Naive Bayesian classifier is the least sensitive to missing data. For the popular missing data treatment methods using prediction model to handle missing data, Naive Bayesian classifier will be preferred.