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<ArticleSet>
<Article>
<Journal>
<PublisherName>OICC Press</PublisherName>
<JournalTitle>Fuzzy Optimization and Modeling Journal (FOMJ)</JournalTitle>
<Issn>2676-7007</Issn>
<Volume>5</Volume>
<Issue>2</Issue>
<PubDate PubStatus="epublish">
<Year>2024</Year>
<Month>06</Month>
<Day>29</Day>
</PubDate>
</Journal>
<ArticleTitle>Ridge Regression With Intuitionistic Fuzzy Input and Output‎: ‎A Parametric Approach</ArticleTitle>
<VernacularTitle></VernacularTitle>
<FirstPage>19</FirstPage>
<LastPage>31</LastPage>
<ELocationID EIdType="doi">10.71808/FOMJ.2024.1002660</ELocationID>
<Language>EN</Language>
<AuthorList>
<Author>
<FirstName>Zahra</FirstName>
<LastName>Behdani</LastName>
<Affiliation>Department of Mathematics, Behbahan Khatam Alanbia University of Technology,
Khouzestan, Iran</Affiliation>
<Identifier Source="ORCID"></Identifier>
</Author>
<Author>
<FirstName>Majid</FirstName>
<LastName>Darehmiraki</LastName>
<Affiliation>Department of Mathematics, Behbahan Khatam Alanbia University of Technology,
Khouzestan, Iran</Affiliation>
<Identifier Source="ORCID"></Identifier>
</Author>
</AuthorList>
<PublicationType>Journal Article</PublicationType>
<History>
<PubDate PubStatus="received">
<Year>2024</Year>
<Month>06</Month>
<Day>29</Day>
</PubDate>
</History>
<Abstract>Ridge regression is a model that is frequently used and has numerous effective applications‎, ‎particularly in the management of correlated factors in a multiple regression model‎. ‎Additionally‎, ‎multicollinearity poses a significant risk in fuzzy regression models when it comes to predictions‎. ‎In order to solve this problem‎, ‎we bring together the fuzzy regression model with the ridge regression technique‎. ‎Regarding the evaluation of the coefficients of the ridge fuzzy regression model‎, ‎the algorithm that we have suggested makes use of the parametric estimation approach‎. ‎In this article‎, ‎we examine the ridge regression in the intuitionistic fuzzy environment‎. ‎We assume that the input and output data are intuitionistic fuzzy numbers‎. ‎Since in the regression analysis we need to calculate the distance between the variables‎, ‎we define a new fuzzy parametric distance‎. ‎Also‎, ‎the goodness of fit of the model with the indicators of the mean square of the prediction error has been investigated in simulation examples and real data‎.</Abstract>
<ObjectList>
<Object Type="keyword">
<Param Name="value">Intuitionistic Fuzzy Number</Param>
</Object>
<Object Type="keyword">
<Param Name="value">Regression Model</Param>
</Object>
<Object Type="keyword">
<Param Name="value">Ridge regression</Param>
</Object>
<Object Type="keyword">
<Param Name="value">Distance</Param>
</Object>
</ObjectList>
</Article>
</ArticleSet>