Abstract
We consider two types of probabilistic approximations, concept and global, applied for mining incomplete data with two interpretations of missing attribute values, lost values and “do not care” conditions. Concept probabilistic approximations were frequently used for mining incomplete data. On the other hand, global probabilistic approximations are introduced in this paper, though experiments with a previous version of the global probabilistic approximations were discussed recently. Global probabilistic approximations are closer to the original concepts than the concept probabilistic approximations. Hence, the quality of global probabilistic approximations, compared with the concept probabilistic approximations, evaluated by tenfold cross-validation, should be higher. However, the results of experiments reported in this paper show that concept probabilistic approximations are better than global probabilistic approximations for lost values.
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References
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Clark, P.G., Grzymala-Busse, J.W., Mroczek, T., Niemiec, R. (2020). Mining Incomplete Data—A Comparison of Concept and New Global Probabilistic Approximations. In: Czarnowski, I., Howlett, R., Jain, L. (eds) Intelligent Decision Technologies 2019. Smart Innovation, Systems and Technologies, vol 142. Springer, Singapore. https://doi.org/10.1007/978-981-13-8311-3_15
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DOI: https://doi.org/10.1007/978-981-13-8311-3_15
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