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dc.contributor.authorDelimata, Paweł
dc.contributor.authorMarszał-Paszek, Barbara
dc.contributor.authorMoshkov, Mikhail
dc.contributor.authorPaszek, Piotr
dc.contributor.authorSkowron, Andrzej
dc.contributor.authorSuraj, Zbigniew
dc.date.accessioned2015-08-04T06:20:59Z
dc.date.available2015-08-04T06:20:59Z
dc.date.issued2010
dc.identifier.citationDelimata, 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
dc.identifier.isbn3642144667; 9783642144660
dc.identifier.issn03029743
dc.identifier.doi10.1007/978-3-642-14467-7_5
dc.identifier.urihttp://hdl.handle.net/10754/564258
dc.description.abstractWe 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.
dc.publisherSpringer Nature
dc.subjectclassification
dc.subjectdecision tables
dc.subjectdeterministic decision rules
dc.subjectinhibitory decision rules
dc.subjectlazy classification algorithms (classifiers)
dc.subjectnondeterministic decision rules
dc.subjectrough sets
dc.subjectrule based classifiers
dc.titleComparison of some classification algorithms based on deterministic and nondeterministic decision rules
dc.typeConference Paper
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentExtensions of Dynamic Programming, Machine Learning and Discrete Optimization Research Group
dc.identifier.journalLecture Notes in Computer Science
dc.conference.date1 May 2008 through 1 May 2008
dc.conference.nameRough Set and Knowledge Technology Conference, RSKT 2008
dc.conference.locationChengdu
dc.contributor.institutionDepartment of Computer Science, University of Rzeszów, Rejtana 16A, 35-310 Rzeszów, Poland
dc.contributor.institutionInstitute of Computer Science, University of Silesia, Bȩdzińska 39, 41-200 Sosnowiec, Poland
dc.contributor.institutionInstitute of Mathematics, Warsaw University, Banacha 2, 02-097 Warsaw, Poland
kaust.personMoshkov, Mikhail


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