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Nondeterministic decision rules in classification process for medical data

In the paper, we discuss nondeterministic rules in decision tables, called the second type nondeterministic rules. They have a few decisions values on the right hand side but on the left hand side only one attribute that has two values. We show that these kinds of rules can be used for improving the quality of classification. It is important in rule-based diagnosis support systems, where classification error can lead to serious consequences. The well known greedy strategy to construct the new nondeterministic rules, have been proposed. Additionally, based on deterministic and nondeterministic (second type) rules, classification algorithm with polynomial computational complexity has been developed. This rule-based classifier was tested on the group of decision tables, containing medical data, from the UCI Machine Learning Repository. The reported results of experiments showing that by combining rule-based classifier based on deterministic rules with second type nondeterministic rules give us possibility to improve the classification quality.