A development of fuzzy-rule-based regression models through using decision trees
Küçük Resim Yok
Tarih
2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
IEEE-inst electrical electronics engineers inc
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
This 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.
Açıklama
Anahtar Kelimeler
Regression Tree Analysis, Predictive Models, Data Models, Prediction Algorithms, Biological System Modeling, Training Data, Takagi-Sugeno Model, Correlation, Decision Tree, Fuzzy Rule-Based Model, Partial Distance Strategy, Regression
Kaynak
IEEE Transactions on Fuzzy Systems
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
32
Sayı
5
Künye
Zhu, 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.
