Abstract
We discuss incomplete data sets with two interpretations of missing attribute values, lost values and “do not care” conditions. For data mining we use two probabilistic approximations, concept and global. Concept probabilistic approximations are well known while global probabilistic approximations are introduced in this paper. The rationale for introducing global probabilistic approximations is a common opinion of the rough set community that global probabilistic approximations, as closer to the approximated concept, should be more successful. Surprisingly, results of our experiments show that the error rate evaluated by ten-fold cross validation is smaller for concept probabilistic approximations than for global probabilistic approximations.
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Clark, P.G., Gao, C., Grzymala-Busse, J.W., Mroczek, T., Niemiec, R. (2018). A Comparison of Concept and Global Probabilistic Approximations Based on Mining Incomplete Data. In: Damaševičius, R., Vasiljevienė, G. (eds) Information and Software Technologies. ICIST 2018. Communications in Computer and Information Science, vol 920. Springer, Cham. https://doi.org/10.1007/978-3-319-99972-2_26
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DOI: https://doi.org/10.1007/978-3-319-99972-2_26
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