IEEE Systems, Man and Cybernetics Magazine - April 2023 - 39
Proposing Datasets for Future Research ers
Synthetic Datasets
for Numeric
Uncertainty
Quantification
Subrota Kumar Mondal
I
by H M Dipu Kabir , Moloud Abdar , Abbas Khosravi , Darius Nahavandi,
, Sadia Khanam, Shady Mohamed, Dipti Srinivasan,
, Ponnuthurai Nagaratnam Suganthan
Saeid Nahavandi
n this article, we propose ten synthetic datasets for
point prediction and numeric uncertainty quantification
(UQ). These datasets are split into the train,
validation, and test sets for model benchmarking.
Equations and the description of each dataset are
provided in detail. We also present representative shallow
Digital Object Identifier 10.1109/MSMC.2022.3218423
Date of current version: 25 April 2023
neural network (NN) training and random vector functional
link (RVFL) training examples both of which are training
models for the point prediction. We perform UQ with
the consideration of a Gaussian and homoscedastic distribution.
Distribution considerations and models are made
quite simple for the following reasons: 1) much room exists
for further explorations and improvements, 2) users of the
dataset have simple training examples including the process
of accessing data, and 3) users get an idea of probable
2333-942X/23©2023IEEE
April 2023 IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE 39
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https://orcid.org/0000-0002-3395-1772
https://orcid.org/0000-0002-3059-6357
https://orcid.org/0000-0001-6927-0744
https://orcid.org/0000-0002-0008-7797
https://orcid.org/0000-0002-0360-5270
https://orcid.org/0000-0003-0901-5105
IEEE Systems, Man and Cybernetics Magazine - April 2023
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