Comparison of Heuristics for Inhibitory Rule Optimization

Handle URI:
http://hdl.handle.net/10754/550701
Title:
Comparison of Heuristics for Inhibitory Rule Optimization
Authors:
Alsolami, Fawaz ( 0000-0001-5858-4908 ) ; Chikalov, Igor; Moshkov, Mikhail ( 0000-0003-0085-9483 )
Abstract:
Knowledge representation and extraction are very important tasks in data mining. In this work, we proposed a variety of rule-based greedy algorithms that able to obtain knowledge contained in a given dataset as a series of inhibitory rules containing an expression “attribute ≠ value” on the right-hand side. The main goal of this paper is to determine based on rule characteristics, rule length and coverage, whether the proposed rule heuristics are statistically significantly different or not; if so, we aim to identify the best performing rule heuristics for minimization of rule length and maximization of rule coverage. Friedman test with Nemenyi post-hoc are used to compare the greedy algorithms statistically against each other for length and coverage. The experiments are carried out on real datasets from UCI Machine Learning Repository. For leading heuristics, the constructed rules are compared with optimal ones obtained based on dynamic programming approach. The results seem to be promising for the best heuristics: the average relative difference between length (coverage) of constructed and optimal rules is at most 2.27% (7%, respectively). Furthermore, the quality of classifiers based on sets of inhibitory rules constructed by the considered heuristics are compared against each other, and the results show that the three best heuristics from the point of view classification accuracy coincides with the three well-performed heuristics from the point of view of rule length minimization.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Comparison of Heuristics for Inhibitory Rule Optimization 2014, 35:378 Procedia Computer Science
Publisher:
Elsevier
Journal:
Procedia Computer Science
Issue Date:
13-Sep-2014
DOI:
10.1016/j.procs.2014.08.118
Type:
Article
ISSN:
18770509
Additional Links:
http://linkinghub.elsevier.com/retrieve/pii/S1877050914010837
Appears in Collections:
Articles; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorAlsolami, Fawazen
dc.contributor.authorChikalov, Igoren
dc.contributor.authorMoshkov, Mikhailen
dc.date.accessioned2015-04-27T13:55:53Zen
dc.date.available2015-04-27T13:55:53Zen
dc.date.issued2014-09-13en
dc.identifier.citationComparison of Heuristics for Inhibitory Rule Optimization 2014, 35:378 Procedia Computer Scienceen
dc.identifier.issn18770509en
dc.identifier.doi10.1016/j.procs.2014.08.118en
dc.identifier.urihttp://hdl.handle.net/10754/550701en
dc.description.abstractKnowledge representation and extraction are very important tasks in data mining. In this work, we proposed a variety of rule-based greedy algorithms that able to obtain knowledge contained in a given dataset as a series of inhibitory rules containing an expression “attribute ≠ value” on the right-hand side. The main goal of this paper is to determine based on rule characteristics, rule length and coverage, whether the proposed rule heuristics are statistically significantly different or not; if so, we aim to identify the best performing rule heuristics for minimization of rule length and maximization of rule coverage. Friedman test with Nemenyi post-hoc are used to compare the greedy algorithms statistically against each other for length and coverage. The experiments are carried out on real datasets from UCI Machine Learning Repository. For leading heuristics, the constructed rules are compared with optimal ones obtained based on dynamic programming approach. The results seem to be promising for the best heuristics: the average relative difference between length (coverage) of constructed and optimal rules is at most 2.27% (7%, respectively). Furthermore, the quality of classifiers based on sets of inhibitory rules constructed by the considered heuristics are compared against each other, and the results show that the three best heuristics from the point of view classification accuracy coincides with the three well-performed heuristics from the point of view of rule length minimization.en
dc.publisherElsevieren
dc.relation.urlhttp://linkinghub.elsevier.com/retrieve/pii/S1877050914010837en
dc.rightsThis is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/3.0/ ).en
dc.subjectinhibitory rulesen
dc.subjectrule heuristicsen
dc.subjectrule lengthen
dc.subjectrule coverageen
dc.titleComparison of Heuristics for Inhibitory Rule Optimizationen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalProcedia Computer Scienceen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionComputer Science Department, King Abdulaziz University, Saudi Arabiaen
kaust.authorAlsolami, Fawazen
kaust.authorChikalov, Igoren
kaust.authorMoshkov, Mikhailen
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