Abstract
In the chapter, we discuss classifiers based on rough set theory and nondeterministic decision rules. We used two kinds of nondeterministic rules called the first and second type. These rules have a few decision values but the rules of the second type can have on the left-hand side one generalized descriptor. i.e., a condition of the form a ∈ V, where V is a two-element subset of the attribute value set V a . We show that these kinds of rules can be used for improving the quality of classification and we propose classifications algorithms based on nondeterministic (first and second type) rules. These algorithms are using not only nondeterministic rules but also minimal rules in the sense of rough sets. In the chapter, these classifiers were tested on several data sets from the UCI Machine Learning Repository and the results were compared. The reported results of experiments show that the proposed classifiers based on nondeterministic rules can improve the classification quality but it requires tuning some of their parameters relative to analyzed data.
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Marszał-Paszek, B., Paszek, P. (2013). Classifiers Based on Nondeterministic Decision Rules. In: Skowron, A., Suraj, Z. (eds) Rough Sets and Intelligent Systems - Professor Zdzisław Pawlak in Memoriam. Intelligent Systems Reference Library, vol 43. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30341-8_23
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DOI: https://doi.org/10.1007/978-3-642-30341-8_23
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