An OWA-based approach to quantile fuzzy regression

Chachi, Jalal; Chaji, Alireza

doi:10.1016/j.cie.2021.107498

Cited by 7 publications

(2 citation statements)

References 59 publications

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“…The general form of a fuzzy regression model for crisp/ fuzzy-input data and fuzzy-output data can be expressed as follows (D'Urso and Gastaldi 2000;D'Urso 2003;D'Urso et al 2010;Wang et al 2020;Hesamian and Akbari 2021;Chachi and Chaji 2021; where 1.…”

Section: Fuzzy Regressionmentioning

confidence: 99%

“…Using the weighted operators and weighted aggregation of squared errors or deviations, Chachi (2019) generalized many simple and natural aggregation types in fuzzy regression modeling, such as the arithmetic mean used in the LS and LAD methods as follows where i , i = 1, … , n are the weight values of individual i th must be properly determined (Chachi and Chaji 2021;Chachi and Taheri 2021;Taheri and Chachi 2021). Therefore, using a well-known distance D between fuzzy numbers, too many studies can be proposed in the framework of fuzzy regression modeling where any combination of the input variables, coefficients and output variable could be "fuzzy" or "crisp".…”

Section: Fuzzy Regressionmentioning

confidence: 99%

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MADM approach to analyse the performance of fuzzy regression models

Kazemifard

Chachi

2021

J Ambient Intell Human Comput

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It is worth to note that estimated parameters of any fuzzy regression model as well as its goodness-of-fit value depend on the objective function being optimized. Thus, it is not easy to compare the solutions of different models that optimize different objective functions. On the other hand, dataset has a significant influence on the performance of any model estimation procedure making the comparison more challengeable. In this paper, we propose a method in order to compare and/or rank a set of fuzzy regression models based on their goodness-of-fit performance analysis when applied to modeling a dataset. With regard to that, the problem of finding the most preferred/desirable model is considered as a multi-attribute decision making (MADM) problem in which the set of candidate models are alternatives and the goodness-of-fit criteria are attributes. Attribute values are calculated based on well-known goodness-of-fit formula while their weights, which are considered as the most important points in MADM problems, are calculated by the entropy measure. Therefore, the entries of the decision matrix are considered as the fitness values of the data points by the models (or alternatives). The entropy values are then transformed to the weight values of the data points illustrating the importance of each data point in the estimation methods of models. Ultimately, the TOPSIS method is employed to provide an algorithm to rank the models. The proposed method not only gives us the rank of the models, but also provides us an index showing how much each ranked model fits the data set.

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