Enhancing Education Policy Estimation: A Novel Ridge Fuzzy Regression Approach for Handling Multicollinearity with Fuzzy Input Data
subject: Education Policy; Multicollinearity; Multiple Fuzzy Linear Regression; Ridge Fuzzy Regression Model; Ridge Regression Model
description: Multicollinearity often complicates regression analysis, both in classical and fuzzy input setup. This research introduces a new approach that combines ridge regression with fuzzy regression to tackle correlated covariates impact, with a specific focus on improving education policy systems. Our method utilizes the ?-level estimation algorithm and a dataset where Grade Point Average (GPA) serves as a fuzzy input, while input variables remain crisp. We assess our estimators performance using RMSE and MAPE. This applied research showcases the potential of our method in enhancing education policies through more accurate data-driven decision-making. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
creator: Das, Tanmoy; Joshi, Hemlata
source: Lecture Notes in Networks and Systems;Volume;1231 LNNS;pp.149-160
publisher: Springer Science and Business Media Deutschland GmbH
date: 01-01-2025
rights: Restricted Access; Hardcopy may be available in the library
relation: ISSN: 23673370; ISBN: 978-303178945-8;
language: English
type: Conference paper
coverage: Das T., Department of Statistics and Data Science, Christ (Deemed to be University), Bengaluru, India; Joshi H., Department of Statistics and Data Science, Christ (Deemed to be University), Bengaluru, India