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Principal Component Analysis of Triangular Fuzzy Number Data

2009
Principal component analysis (PCA) is a well-known tool often used for the exploratory analysis of a data set, which can be used to reduce the data dimensionality and also to decrease the dependency among features. The traditional PCA algorithms are designed aiming at numerical data instead of non-numerical data.
Na-xin Chen, Yun-jie Zhang
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Fuzzy Logistic Regression with Triangular and Gaussian Fuzzy Numbers

This thesis presents a comprehensive exploration of fuzzy methods for binary classification problems, focusing on addressing the critical challenges of class imbalance, complete separation, multicollinearity, seasonality, and efficiency. The particular areas of focus for the application in each chapter are varied, including environmental problems ...
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IS TRIANGULAR FUZZY NUMBERS OR TRAPEZOIDAL FUZZY NUMBERS AS MEMBERSHIP FUNCTION?

2007
Fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) yöntemi belirsiz ortamlarda Çok Kriterli Karar Verme (ÇKKV) yöntemlerinden birisidir ve grup kararı vermede kullanılır. Fuzzy TOPSIS yönteminin temelinde ideal çözümün Bulanık Pozitif İdeal Çözümden (BPİÇ) en yakın, Bulanık Negatif İdeal Çözümden (BNİÇ) ise en uzak mesafede ...
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Conflict analysis based on three-way decision for triangular fuzzy information systems

International Journal of Approximate Reasoning, 2021
Huangjian Yi, Guangming Lang
exaly  

A linear regression model using triangular fuzzy number coefficients

Fuzzy Sets and Systems, 1999
S Ghoshray, K K Yen
exaly  

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