A General Framework for Context-Aware Fuzzification of Four Ordered Categories: A Case Study on BMI Categories

This paper presents a general methodological framework for constructing contextaware fuzzy partitions that extend conventional crisp categorizations. The approach is
based on Novák’s theory of fuzzy contexts and is implemented using the R package lfl. It enables smooth and interpretable transitions between adjacent classes while preserving the original categorical structure. To illustrate the procedure, we apply it to derive fitness-specific fuzzy partitions of Body Mass Index, where the conventional four categories (underweight, normal weight, overweight, obese) are adapted according to individual levels of cardiorespiratory fitness.