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

Handle URI:
http://hdl.handle.net/10754/564258
Title:
Comparison of some classification algorithms based on deterministic and nondeterministic decision rules
Authors:
Delimata, Paweł; Marszał-Paszek, Barbara; Moshkov, Mikhail ( 0000-0003-0085-9483 ) ; Paszek, Piotr; Skowron, Andrzej; Suraj, Zbigniew
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.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Applied Mathematics and Computational Science Program; Extensions of Dynamic Programming, Machine Learning and Discrete Optimization Research Group
Publisher:
Springer Science + Business Media
Journal:
Lecture Notes in Computer Science
Conference/Event name:
Rough Set and Knowledge Technology Conference, RSKT 2008
Issue Date:
2010
DOI:
10.1007/978-3-642-14467-7_5
Type:
Conference Paper
ISSN:
03029743
ISBN:
3642144667; 9783642144660
Appears in Collections:
Conference Papers; Applied Mathematics and Computational Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorDelimata, Pawełen
dc.contributor.authorMarszał-Paszek, Barbaraen
dc.contributor.authorMoshkov, Mikhailen
dc.contributor.authorPaszek, Piotren
dc.contributor.authorSkowron, Andrzejen
dc.contributor.authorSuraj, Zbigniewen
dc.date.accessioned2015-08-04T06:20:59Zen
dc.date.available2015-08-04T06:20:59Zen
dc.date.issued2010en
dc.identifier.isbn3642144667; 9783642144660en
dc.identifier.issn03029743en
dc.identifier.doi10.1007/978-3-642-14467-7_5en
dc.identifier.urihttp://hdl.handle.net/10754/564258en
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.en
dc.publisherSpringer Science + Business Mediaen
dc.subjectclassificationen
dc.subjectdecision tablesen
dc.subjectdeterministic decision rulesen
dc.subjectinhibitory decision rulesen
dc.subjectlazy classification algorithms (classifiers)en
dc.subjectnondeterministic decision rulesen
dc.subjectrough setsen
dc.subjectrule based classifiersen
dc.titleComparison of some classification algorithms based on deterministic and nondeterministic decision rulesen
dc.typeConference Paperen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentApplied Mathematics and Computational Science Programen
dc.contributor.departmentExtensions of Dynamic Programming, Machine Learning and Discrete Optimization Research Groupen
dc.identifier.journalLecture Notes in Computer Scienceen
dc.conference.date1 May 2008 through 1 May 2008en
dc.conference.nameRough Set and Knowledge Technology Conference, RSKT 2008en
dc.conference.locationChengduen
dc.contributor.institutionDepartment of Computer Science, University of Rzeszów, Rejtana 16A, 35-310 Rzeszów, Polanden
dc.contributor.institutionInstitute of Computer Science, University of Silesia, Bȩdzińska 39, 41-200 Sosnowiec, Polanden
dc.contributor.institutionInstitute of Mathematics, Warsaw University, Banacha 2, 02-097 Warsaw, Polanden
kaust.authorMoshkov, Mikhailen
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