Asset Details
Do you wish to reserve the book?
A soft computing TOPSIS framework with fractional orthopair fuzzy sine aggregation for robust model evaluation in data science
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
A soft computing TOPSIS framework with fractional orthopair fuzzy sine aggregation for robust model evaluation in data science
Do you wish to request the book?
A soft computing TOPSIS framework with fractional orthopair fuzzy sine aggregation for robust model evaluation in data science
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
A soft computing TOPSIS framework with fractional orthopair fuzzy sine aggregation for robust model evaluation in data science
2026
Overview
Model selection in data science involves evaluating multiple alternatives across conflicting criteria under uncertainty, where existing fuzzy multi criteria group decision making (MCGDM) approaches often fail to capture asymmetric uncertainty and nonlinear interactions in expert judgments. To address this limitation, this study proposes a novel MCGDM framework based on fractional orthopair fuzzy sets (FOFS). The FOFS structure enables flexible and precise modeling of uncertainty by allowing independent fractional control of membership degree (MD) and non-membership degree (NMD). Furthermore, sine trigonometric aggregation operators are introduced to capture nonlinear relationships and fluctuations in expert evaluations. An integrated FOFS–TOPSIS method is then developed to rank candidate models based on their distances from positive ideal solution (PIS) and negative ideal solution (NIS). The applicability of the framework is demonstrated through a numerical study involving fifteen predictive models, fifteen evaluation criteria, and three experts. The results indicate that Alternative
achieved the highest overall ranking, followed by
, while mid and lower ranked models revealed trade-offs in accuracy, computational efficiency, and robustness. Comparative and sensitivity analyses confirm the framework’s robustness, stability, and superior ranking performance.
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
Subject
