Boscovich Fuzzy Regression Line

Boscovich Fuzzy Regression Line

We introduce a new fuzzy linear regression method. The method is capable of approximating fuzzy relationships between an independent and a dependent variable. The independent and dependent variables are expected to be a real value and triangular fuzzy numbers, respectively. We demonstrate on twenty...

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Bibliographic Details
Main Authors: Pavel Škrabánek, Jaroslav Marek, Alena Pozdílková
Format: Article
Language:English
Published: MDPI AG 2021-03-01
Series:Mathematics
Subjects:
fuzzy linear regression
non-symmetric triangular fuzzy number
least absolute value
Boscovich regression line
outlier
Online Access:https://www.mdpi.com/2227-7390/9/6/685
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spelling doaj-8c9ddca71c2b4e12a1d877eef171dcaf2021-03-24T00:01:51ZengMDPI AGMathematics2227-73902021-03-01968568510.3390/math9060685Boscovich Fuzzy Regression LinePavel Škrabánek0Jaroslav Marek1Alena Pozdílková2Institute of Automation and Computer Science, Brno University of Technology, Technická 2896/2, 616 69 Brno, Czech RepublicDepartment of Mathematics and Physics, University of Pardubice, Studentská 95, 532 10 Pardubice, Czech RepublicDepartment of Mathematics and Physics, University of Pardubice, Studentská 95, 532 10 Pardubice, Czech RepublicWe introduce a new fuzzy linear regression method. The method is capable of approximating fuzzy relationships between an independent and a dependent variable. The independent and dependent variables are expected to be a real value and triangular fuzzy numbers, respectively. We demonstrate on twenty datasets that the method is reliable, and it is less sensitive to outliers, compare with possibilistic-based fuzzy regression methods. Unlike other commonly used fuzzy regression methods, the presented method is simple for implementation and it has linear time-complexity. The method guarantees non-negativity of model parameter spreads.https://www.mdpi.com/2227-7390/9/6/685fuzzy linear regressionnon-symmetric triangular fuzzy numberleast absolute valueBoscovich regression lineoutlier
collection DOAJ
language English
format Article
sources DOAJ
author Pavel Škrabánek
Jaroslav Marek
Alena Pozdílková
spellingShingle Pavel Škrabánek
Jaroslav Marek
Alena Pozdílková
Boscovich Fuzzy Regression Line
Mathematics
fuzzy linear regression
non-symmetric triangular fuzzy number
least absolute value
Boscovich regression line
outlier
author_facet Pavel Škrabánek
Jaroslav Marek
Alena Pozdílková
author_sort Pavel Škrabánek
title Boscovich Fuzzy Regression Line
title_short Boscovich Fuzzy Regression Line
title_full Boscovich Fuzzy Regression Line
title_fullStr Boscovich Fuzzy Regression Line
title_full_unstemmed Boscovich Fuzzy Regression Line
title_sort boscovich fuzzy regression line
publisher MDPI AG
series Mathematics
issn 2227-7390
publishDate 2021-03-01
description We introduce a new fuzzy linear regression method. The method is capable of approximating fuzzy relationships between an independent and a dependent variable. The independent and dependent variables are expected to be a real value and triangular fuzzy numbers, respectively. We demonstrate on twenty datasets that the method is reliable, and it is less sensitive to outliers, compare with possibilistic-based fuzzy regression methods. Unlike other commonly used fuzzy regression methods, the presented method is simple for implementation and it has linear time-complexity. The method guarantees non-negativity of model parameter spreads.
topic fuzzy linear regression
non-symmetric triangular fuzzy number
least absolute value
Boscovich regression line
outlier
url https://www.mdpi.com/2227-7390/9/6/685
work_keys_str_mv AT pavelskrabanek boscovichfuzzyregressionline
AT jaroslavmarek boscovichfuzzyregressionline
AT alenapozdilkova boscovichfuzzyregressionline
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