Abstract
The presence of missing values in a dataset can affect the performance of a classifier constructed using that dataset as a training sample. Several methods have been proposed to treat missing data and the one used most frequently deletes instances containing at least one missing value of a feature. In this paper we carry out experiments with twelve datasets to evaluate the effect on the misclassification error rate of four methods for dealing with missing values: the case deletion method, mean imputation, median imputation, and the KNN imputation procedure. The classifiers considered were the Linear Discriminant Analysis (LDA) and the KNN classifier. The first one is a parametric classifier whereas the second one is a nonparametric classifier.
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© 2004 Springer-Verlag Berlin Heidelberg
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Acuña, E., Rodriguez, C. (2004). The Treatment of Missing Values and its Effect on Classifier Accuracy. In: Banks, D., McMorris, F.R., Arabie, P., Gaul, W. (eds) Classification, Clustering, and Data Mining Applications. Studies in Classification, Data Analysis, and Knowledge Organisation. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17103-1_60
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DOI: https://doi.org/10.1007/978-3-642-17103-1_60
Publisher Name: Springer, Berlin, Heidelberg
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