Fuzzy associative classifiers (FACs) have recently received considerable attention in the data mining community due to their ability to address the imprecision and graduality of truth. Similar to their more traditional statistical peers, these classifiers, however, have remained largely data driven, not leveraging human knowledge to their advantage. This is while human expert opinion and intuition should be a unique vantage point for such systems. We introduce here, for the first time, a human-centered framework (FLeAC) for FACs based on extended fuzzy logic and f -transformation that uses experts’ opinions and preferences along with statistical data to solve subjective real-world problems. In FLeAC, experts take part in both constructing and reasoning of the classifier by assigning linguistic validity to each item. These validities are then aggregated using collective intelligence that determines final item validity. To examine the proposed framework, we extend an efficient and well-known FAC, CFAR, and present an extended f -CFAR algorithm. Also, several variations of f -CFAR are implemented to examine the effect of rule validity and different f-transformation operators. We then run various nonparametric statistical tests, including Friedman, Nemenyi posthoc, and ROC tests on an actual medical dataset of burn patients from Ahwaz, Iran, to compare f -CFAR performance with those of the original and nine other rule-based classifiers. Statistical analysis shows that f -CFAR not only has a better overall diagnostic performance than CFAR but also it outperforms CFAR and the other rule-based classifiers in terms of the number of rules, the number of conditions, and the execution time, leading to a more compact and comprehensible classifier with comparable accuracy.

Keywords