Abstract
In the paper, we discuss nondeterministic rules in decision tables, called the truncated nondeterministic rules. These rules have on the right hand side a few decisions. We show that the truncated nondeterministic rules can be used for improving the quality of classification.
We propose a greedy algorithm of polynomial time complexity to construct these rules. We use this type of rules, to build up rule-based classifiers. These classifiers, classification algorithms, are used not only nondeterministic rules but also minimal rules in the sense of rough sets. These rule-based classifiers were tested on the group of decision tables from the UCI Machine Learning Repository. The reported results of the experiment show that the proposed classifiers based on nondeterministic rules improve the classification quality but it requires tuning some of their parameters relative to analyzed data.
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Paszek, P., Marszał-Paszek, B. (2012). Nondeterministic Decision Rules in Classification Process. In: Herrero, P., Panetto, H., Meersman, R., Dillon, T. (eds) On the Move to Meaningful Internet Systems: OTM 2012 Workshops. OTM 2012. Lecture Notes in Computer Science, vol 7567. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33618-8_65
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DOI: https://doi.org/10.1007/978-3-642-33618-8_65
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33617-1
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