Abstract
In this paper incomplete data sets, or data sets with missing attribute values, have two interpretations, lost values and “do not care” conditions. Additionally, the process of data mining is based on two types of probabilistic approximations, global and saturated. We present results of experiments on mining incomplete data sets using four approaches, combining two interpretations of missing attribute values with two types of probabilistic approximations. We compare our four approaches, using the error rate computed as a result of ten-fold cross validation as a criterion of quality. We show that for some data sets the error rate is significantly smaller (5% level of significance) for lost values than for “do not care” conditions, while for other data sets the error rate is smaller for “do not care” conditions. For “do not care” conditions, the error rate is significantly smaller for saturated probabilistic approximations than for global probabilistic approximations for two data sets, for another data set it is the other way around, while for remaining five data sets the difference is insignificant. Thus, for an incomplete data set, the best approach to data mining should be chosen by trying all four approaches.
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Clark, P.G., Grzymala-Busse, J.W., Hippe, Z.S., Mroczek, T., Niemiec, R. (2020). Global and Saturated Probabilistic Approximations Based on Generalized Maximal Consistent Blocks. In: de la Cal, E.A., Villar Flecha, J.R., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2020. Lecture Notes in Computer Science(), vol 12344. Springer, Cham. https://doi.org/10.1007/978-3-030-61705-9_32
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