A novel switching function approach for data mining classification problems

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dc.contributor.authorIbrahim, Mohammed Hussein
dc.contributor.authorHacibeyoglu, Mehmet
dc.date.accessioned2024-02-23T13:43:53Z
dc.date.available2024-02-23T13:43:53Z
dc.date.issued2020
dc.departmentNEÜen_US
dc.description13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) -- JUL 29-31, 2017 -- Guilin, PEOPLES R CHINAen_US
dc.description.abstractRule induction (RI) is one of the known classification approaches in data mining. RI extracts hidden patterns from instances in terms of rules. This paper proposes a logic-based rule induction (LBRI) classifier based on a switching function approach. LBRI generates binary rules by using a novel minimization function, which depends on simple and powerful bitwise operations. Initially, LBRI generates instance codes by encoding the dataset with standard binary code and then generates prime cubes (PC) for all classes from the instance codes by the proposed reduced offset method. Finally, LBRI selects the most effective PC of the current classes and adds them into the binary rule set that belongs to the current class. Each binary rule represents an If-Then rule for the rule induction classifiers. The proposed LBRI classifier is based on basic logic functions. It is a simple and effective method, and it can be used by intelligent systems to solve real-life classification/ prediction problems in areas such as health care, online/financial banking, image/voice recognition, and bioinformatics. The performance of the proposed algorithm is compared to six rule induction algorithms; decision table, Ripper, C4.5, REPTree, OneR, and ICRM by using nineteen different datasets. The experimental results show that the proposed algorithm yields better classification accuracy than the other rule induction algorithms on ten out of nineteen datasets.en_US
dc.description.sponsorshipIEEE,IEEE Circuits & Syst Soc,Guilin Univ Elect Technol,Hunan Univ,Guilin Univ Technol,Guilin Univ Aerosp Technolen_US
dc.identifier.doi10.1007/s00500-019-04246-2
dc.identifier.endpage4957en_US
dc.identifier.issn1432-7643
dc.identifier.issn1433-7479
dc.identifier.issue7en_US
dc.identifier.scopus2-s2.0-85070075023en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage4941en_US
dc.identifier.urihttps://doi.org/10.1007/s00500-019-04246-2
dc.identifier.urihttps://hdl.handle.net/20.500.12452/10960
dc.identifier.volume24en_US
dc.identifier.wosWOS:000530043200016en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofSoft Computingen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectClassificationen_US
dc.subjectRule Inductionen_US
dc.subjectLogic Minimizationen_US
dc.subjectPrime Cubeen_US
dc.subjectData Miningen_US
dc.subjectSwitching Functionen_US
dc.titleA novel switching function approach for data mining classification problemsen_US
dc.typeConference Objecten_US

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