Comparison of some classification algorithms based on deterministic and nondeterministic decision rules

Abstract
We discuss two, in a sense extreme, kinds of nondeterministic rules in decision tables. The first kind of rules, called as inhibitory rules, are blocking only one decision value (i.e., they have all but one decisions from all possible decisions on their right hand sides). Contrary to this, any rule of the second kind, called as a bounded nondeterministic rule, can have on the right hand side only a few decisions. We show that both kinds of rules can be used for improving the quality of classification. In the paper, two lazy classification algorithms of polynomial time complexity are considered. These algorithms are based on deterministic and inhibitory decision rules, but the direct generation of rules is not required. Instead of this, for any new object the considered algorithms extract from a given decision table efficiently some information about the set of rules. Next, this information is used by a decision-making procedure. The reported results of experiments show that the algorithms based on inhibitory decision rules are often better than those based on deterministic decision rules. We also present an application of bounded nondeterministic rules in construction of rule based classifiers. We include the results of experiments showing that by combining rule based classifiers based on minimal decision rules with bounded nondeterministic rules having confidence close to 1 and sufficiently large support, it is possible to improve the classification quality. © 2010 Springer-Verlag.

Citation
Delimata, P., Marszał-Paszek, B., Moshkov, M., Paszek, P., Skowron, A., & Suraj, Z. (2010). Comparison of Some Classification Algorithms Based on Deterministic and Nondeterministic Decision Rules. Transactions on Rough Sets XII, 90–105. doi:10.1007/978-3-642-14467-7_5

Publisher
Springer Nature

Journal
Lecture Notes in Computer Science

Conference/Event Name
Rough Set and Knowledge Technology Conference, RSKT 2008

DOI
10.1007/978-3-642-14467-7_5

Permanent link to this record