Abstract
In this paper, incomplete data sets have missing attribute values of two types: lost values and “do not care” conditions. Our algorithm of data mining, based on rule induction, uses two types of probabilistic approximations, called global and saturated. Thus, we use four different ways of rule induction, applying two types of missing attribute values with two types of probabilistic approximations. We used ten-fold cross validation to estimate an error rate. Previous results, with data sets with 35% of missing attribute values, show that there is no universally best way of rule induction. Therefore, in our current experiments, we use data sets with many missing attribute values. As follows from our new results, the best way of data mining should be selected by running experiments taking into account all four possibilities.
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Clark, P.G., Grzymala-Busse, J.W., Mroczek, T., Niemiec, R. (2020). Mining Data with Many Missing Attribute Values Using Global and Saturated Probabilistic Approximations Based on Characteristic Sets. In: Lopata, A., Butkienė, R., Gudonienė, D., Sukackė, V. (eds) Information and Software Technologies. ICIST 2020. Communications in Computer and Information Science, vol 1283. Springer, Cham. https://doi.org/10.1007/978-3-030-59506-7_7
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