A Fuzzy Logic Rule Based Paradigm for Wine Quality Prediction
Keywords:
Fuzzy Rule Based System, Multiple Linear Regression, MRE, MMRE, Random ForestAbstract
We consistently employ machine learning to uncover fascinating patterns and trends from large and intricate datasets. Utilizing supervised machine learning to classify real-life data is a common practice. This study employs multiple linear regression (MLR), random forest (RF), and fuzzy rule-based systems (FRBS) to classify wine quality and evaluate performance metrics. A FRBS demonstrates high accuracy. The calculations for magnitude of relative error (MRE) and mean magnitude of relative error (MMRE) demonstrate the achievement of model perfection. The MRE and MMRE achieved through a fuzzy rule-based system (FRBS) are lower than those obtained using MLR and Random Forest. The fuzzy rule-based system, multiple linear regression, and fuzzy logic system demonstrate MRE values of 0.1312 and MMRE values of 0.0043, respectively. This investigation utilizes the white wine dataset from the UCI Machine Learning Repository. Fuzzy logic represents a cutting-edge approach to creating wine quality prediction models. This research presents a fuzzy logic system that forecasts wine quality. Additionally, evaluate the system's performance in relation to MLR and random forest models. The results show that the MMRE value derived from fuzzy logic is less than the MMRE value derived from MLR. Furthermore, the values of Pred (0.25) and Pred (0.05) derived from fuzzy logic beat those achieved through multiple linear regression and random forest techniques.
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