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Computer Science > Machine Learning

arXiv:2109.05461 (cs)
COVID-19 e-print

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[Submitted on 12 Sep 2021]

Title:Accurate Prediction Using Triangular Type-2 Fuzzy Linear Regression

Authors:Assef Zare, Afshin Shoeibi, Narges Shafaei, Parisa Moridian, Roohallah Alizadehsani, Majid Halaji, Abbas Khosravi
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Abstract:Many works have been done to handle the uncertainties in the data using type 1 fuzzy regression. Few type 2 fuzzy regression works used interval type 2 for indeterminate modeling using type 1 fuzzy membership. The current survey proposes a triangular type-2 fuzzy regression (TT2FR) model to ameliorate the efficiency of the model by handling the uncertainty in the data. The triangular secondary membership function is used instead of widely used interval type models. In the proposed model, vagueness in primary and secondary fuzzy sets is minimized and also, a specified x-plane of observed value is included in the same {\alpha}- plane of the predicted value. Complex calculations of the type-2 fuzzy (T2F) model are simplified by reducing three dimensional type-2 fuzzy set (3DT2FS) into two dimensional interval type-2 fuzzy (2DIT2F) models. The current survey presents a new regression model of T2F by considering the more general form of T2F membership functions and thus avoids high complexity. The performance of the developed model is evaluated using the TAIEX and COVID-19 forecasting datasets. Our developed model reached the highest performance as compared to the other state-of-art techniques. Our developed method is ready to be tested with more uncertain data and has the potential to use to predict the weather and stock prediction.
Subjects: Machine Learning (cs.LG)
Cite as: arXiv:2109.05461 [cs.LG]
  (or arXiv:2109.05461v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2109.05461

Submission history

From: Roohallah Alizadehsani Dr [view email]
[v1] Sun, 12 Sep 2021 08:34:14 UTC (1,002 KB)
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