Abstract
This paper is a continuation of our previous research in which we compared four classification strategies using rule sets induced from incomplete data sets and global probabilistic approximations. In our current research we use local probabilistic approximations. In our incomplete data sets, missing attribute values are interpreted as lost values and “do not care” conditions. Our current results are that for symbolic data and numerical data with a few attributes, the best strategy is strength with support, while for data sets with many numerical attributes the best strategy is probability only. Our results for incomplete data sets with many numerical attributes are supported by only three data sets so further research is required.
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Clark, P.G., Gao, C., Grzymala-Busse, J.W. (2017). A Comparison of Four Classification Systems Using Rule Sets Induced from Incomplete Data Sets by Local Probabilistic Approximations. In: Kryszkiewicz, M., Appice, A., Ślęzak, D., Rybinski, H., Skowron, A., Raś, Z. (eds) Foundations of Intelligent Systems. ISMIS 2017. Lecture Notes in Computer Science(), vol 10352. Springer, Cham. https://doi.org/10.1007/978-3-319-60438-1_28
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