Decision and Inhibitory Rule Optimization for Decision Tables with Many-valued Decisions

Handle URI:
http://hdl.handle.net/10754/607196
Title:
Decision and Inhibitory Rule Optimization for Decision Tables with Many-valued Decisions
Authors:
Alsolami, Fawaz ( 0000-0001-5858-4908 )
Abstract:
‘If-then’ rule sets are one of the most expressive and human-readable knowledge representations. This thesis deals with optimization and analysis of decision and inhibitory rules for decision tables with many-valued decisions. The most important areas of applications are knowledge extraction and representation. The benefit of considering inhibitory rules is connected with the fact that in some situations they can describe more knowledge than the decision ones. Decision tables with many-valued decisions arise in combinatorial optimization, computational geometry, fault diagnosis, and especially under the processing of data sets. In this thesis, various examples of real-life problems are considered which help to understand the motivation of the investigation. We extend relatively simple results obtained earlier for decision rules over decision tables with many-valued decisions to the case of inhibitory rules. The behavior of Shannon functions (which characterize complexity of rule systems) is studied for finite and infinite information systems, for global and local approaches, and for decision and inhibitory rules. The extensions of dynamic programming for the study of decision rules over decision tables with single-valued decisions are generalized to the case of decision tables with many-valued decisions. These results are also extended to the case of inhibitory rules. As a result, we have algorithms (i) for multi-stage optimization of rules relative to such criteria as length or coverage, (ii) for counting the number of optimal rules, (iii) for construction of Pareto optimal points for bi-criteria optimization problems, (iv) for construction of graphs describing relationships between two cost functions, and (v) for construction of graphs describing relationships between cost and accuracy of rules. The applications of created tools include comparison (based on information about Pareto optimal points) of greedy heuristics for bi-criteria optimization of rules, and construction (based on multi-stage optimization of rules) of relatively short systems of rules that can be used for knowledge representation.
Advisors:
Moshkov, Mikhail ( 0000-0003-0085-9483 )
Committee Member:
Bajic, Vladimir B. ( 0000-0001-5435-4750 ) ; Suraj, Zbigniew; Zhang, Xiangliang ( 0000-0002-3574-5665 )
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computer Science
Program:
Computer Science
Issue Date:
25-Apr-2016
Type:
Dissertation
Appears in Collections:
Dissertations; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.advisorMoshkov, Mikhailen
dc.contributor.authorAlsolami, Fawazen
dc.date.accessioned2016-04-27T07:56:10Zen
dc.date.available2016-04-27T07:56:10Zen
dc.date.issued2016-04-25en
dc.identifier.urihttp://hdl.handle.net/10754/607196en
dc.description.abstract‘If-then’ rule sets are one of the most expressive and human-readable knowledge representations. This thesis deals with optimization and analysis of decision and inhibitory rules for decision tables with many-valued decisions. The most important areas of applications are knowledge extraction and representation. The benefit of considering inhibitory rules is connected with the fact that in some situations they can describe more knowledge than the decision ones. Decision tables with many-valued decisions arise in combinatorial optimization, computational geometry, fault diagnosis, and especially under the processing of data sets. In this thesis, various examples of real-life problems are considered which help to understand the motivation of the investigation. We extend relatively simple results obtained earlier for decision rules over decision tables with many-valued decisions to the case of inhibitory rules. The behavior of Shannon functions (which characterize complexity of rule systems) is studied for finite and infinite information systems, for global and local approaches, and for decision and inhibitory rules. The extensions of dynamic programming for the study of decision rules over decision tables with single-valued decisions are generalized to the case of decision tables with many-valued decisions. These results are also extended to the case of inhibitory rules. As a result, we have algorithms (i) for multi-stage optimization of rules relative to such criteria as length or coverage, (ii) for counting the number of optimal rules, (iii) for construction of Pareto optimal points for bi-criteria optimization problems, (iv) for construction of graphs describing relationships between two cost functions, and (v) for construction of graphs describing relationships between cost and accuracy of rules. The applications of created tools include comparison (based on information about Pareto optimal points) of greedy heuristics for bi-criteria optimization of rules, and construction (based on multi-stage optimization of rules) of relatively short systems of rules that can be used for knowledge representation.en
dc.language.isoenen
dc.subjectrule optimizationen
dc.subjectdecision rulesen
dc.subjectinhibitory rulesen
dc.subjectDynamic Programmingen
dc.subjectdecision tables with many valued decisionsen
dc.titleDecision and Inhibitory Rule Optimization for Decision Tables with Many-valued Decisionsen
dc.typeDissertationen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentComputer Scienceen
thesis.degree.grantorKing Abdullah University of Science and Technologyen_GB
dc.contributor.committeememberBajic, Vladimir B.en
dc.contributor.committeememberSuraj, Zbigniewen
dc.contributor.committeememberZhang, Xiangliangen
thesis.degree.disciplineComputer Scienceen
thesis.degree.nameDoctor of Philosophyen
dc.person.id118592en
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