A development of fuzzy-rule-based regression models through using decision trees

Basit Öğe Kaydı
dc.authorscopusidWitold Pedrycz / 56854903200
dc.authorwosidWitold Pedrycz / FPE-7309-2022
dc.contributor.authorZhu, Xiubin
dc.contributor.authorHu, Xingchen
dc.contributor.authorYang, Lan
dc.contributor.authorPedrycz, Witol
dc.contributor.authorLi, Zhiwu
dc.date.accessioned2025-04-18T08:25:24Z
dc.date.available2025-04-18T08:25:24Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractThis article presents a design and realization of fuzzy rule-based regression models based on standard decision trees. A two-phase design of rule-based model is offered in this study to provide a good alternative to cope with high dimensional data. We first build a standard decision tree on the basis of variables in order to discover homogeneous subsets of the data. Subsequently, a collection of fuzzy rules is induced by the decision tree with the aim of reflecting the underlying phenomenon. The calculation of membership degrees and the refinement of fuzzy rules on the basis of data located in each partition exhibit a substantial level of originality and innovation. The introduction of fuzziness into decision rules helps to characterize and quantify the continuous change of output values near the boundary areas. The constructed fuzzy rules could efficiently handle the ambiguity and vagueness in the experimental evidence and offer an accurate characterization of the nonlinearities of the input-output relationships. The developed fuzzy models could achieve much higher prediction accuracy in comparison with traditional decision trees of the same size and fuzzy rule-based models with the same number of rules. Another advantage of the proposed methodology comes with the evident readability of the formed fuzzy rules. A series of experiments is reported to demonstrate the superiority of the proposed architecture of fuzzy rule-based models over traditional fuzzy rule-based models and decision trees.
dc.identifier.citationZhu, X., Hu, X., Yang, L., Pedrycz, W., & Li, Z. (2024). A Development of Fuzzy Rule-based Regression Models through Using Decision Trees. IEEE Transactions on Fuzzy Systems.
dc.identifier.doi10.1109/TFUZZ.2024.3365572
dc.identifier.endpage2986
dc.identifier.issn1063-6706
dc.identifier.issn1941-0034
dc.identifier.issue5
dc.identifier.scopus2-s2.0-85185369169
dc.identifier.scopusqualityQ1
dc.identifier.startpage2976
dc.identifier.urihttp://dx.doi.org/10.1109/TFUZZ.2024.3365572
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6561
dc.identifier.volume32
dc.identifier.wosWOS:001214545400002
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorPedrycz, Witol
dc.institutionauthoridWitold Pedrycz / 0000-0002-9335-9930
dc.language.isoen
dc.publisherIEEE-inst electrical electronics engineers inc
dc.relation.ispartofIEEE Transactions on Fuzzy Systems
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectRegression Tree Analysis
dc.subjectPredictive Models
dc.subjectData Models
dc.subjectPrediction Algorithms
dc.subjectBiological System Modeling
dc.subjectTraining Data
dc.subjectTakagi-Sugeno Model
dc.subjectCorrelation
dc.subjectDecision Tree
dc.subjectFuzzy Rule-Based Model
dc.subjectPartial Distance Strategy
dc.subjectRegression
dc.titleA development of fuzzy-rule-based regression models through using decision trees
dc.typeArticle

Dosyalar

Lisans paketi
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
license.txt
Boyut:
1.17 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

Bilgisayar Mühendisliği Bölümü Makale Koleksiyonu