Abstract
Common problem in data classification is the incompleteness of the data, and not always it is possible to re-acquire the missing values. Another approach is to fill-in missing values using some statistical methods. This however distracts the original data and may lead to over-fit the classifier to the artificially generated values, and in consequence to overestimate the classifier accuracy in Cross Validation tests. In this paper we propose a solution where, for a reference data consisting of complete and incomplete records, complete records serve as a reference data for a standard classifier, while the whole set serves as a reference data for single feature subspaced classifier.
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Orczyk, T., Doroz, R., Porwik, P. (2020). Combined kNN Classifier for Classification of Incomplete Data. In: Burduk, R., Kurzynski, M., Wozniak, M. (eds) Progress in Computer Recognition Systems. CORES 2019. Advances in Intelligent Systems and Computing, vol 977. Springer, Cham. https://doi.org/10.1007/978-3-030-19738-4_3
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DOI: https://doi.org/10.1007/978-3-030-19738-4_3
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