Abstract
Rule-Based Fuzzy Cognitive Maps (RBFCM) extend Fuzzy Cognitive Maps by incorporating fuzzy rule-based reasoning, enabling the modelling of complex qualitative systems with causal feedback. However, the standard reasoning mechanisms of RBFCM, designed for variation-based domains, exhibit poor performance when applied to level-based domains, such as knowledge modelling of learners, due to their reliance on assumptions about fuzzy set construction. This paper proposes enhancements to the RBFCM reasoning mechanism by introducing an Impact Strength (iS) parameter that explicitly represents the strength of influence between concepts and improves the construction of the Influence Output Set (IOS). Furthermore, this paper also introduces a new shifting mechanism and a simplified impact accumulation process, ensuring semantic consistency, preserving fuzziness, and preventing impact saturation. Experiments on a real learner dataset demonstrate that the enhanced RBFCM significantly outperforms the standard RBFCM, achieving an accuracy of 85.29%, a 28% improvement, with a higher F1-score and lower RMSE and standard deviation of error. These results confirm that the proposed enhancements enable RBFCM to model level-based knowledge domains effectively while maintaining interpretability and robustness.
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