Paper desribes results of an experiment where various scenarios of missing values occurrence in the data repository has been tested. Experiment was coducted on a publicly available database, containing complete, multidimensional continuous dataspace and multiple classes. Missing values were introduced using “completely at random” scheme. Tested scenarios were: training and testing using incomplete dataset, training on complete data set and testing on incomplete and vice versa. For comparison to data imputation methods also the ensemble of single-feature kNN classifiers, working withoud data imputation, has been tested.
CITATION STYLE
Orczyk, T., & Porwik, P. (2015). Investigation of the impact of missing value imputation methods on the k-nn classification accuracy. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9330 LNCS, pp. 557–565). Springer Verlag. https://doi.org/10.1007/978-3-319-24306-1_54
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