IFC-BD: An Interpretable Fuzzy Classifier for Boosting Explainable Artificial Intelligence in Big Data

Abstract

In current Data Science applications, the course of action has derived to adapt the system behavior for the human cognition, resulting in the emerging area of explainable artificial intelligence. Among different classification paradigms, those based on fuzzy rules are suitable solutions to stress the interpretability of the global systems. However, in case of addressing Big Data analytics, they may comprise an excessive number of rules and/or linguistic labels that not only may cause losing the system performance but also may affect the system semantic as well as the system interpretability. In this article, we propose IFC-BD, an interpretable fuzzy classifier for Big Data, aiming at boosting the horizons of explainability by learning a compact yet accurate fuzzy model. IFC-BD is developed in a cell-based distributed framework through the three working stages of initial rule learning, rule generalization, and heuristic rule selection. This whole procedure allows reaching from a high number of specific rules to less number of more general and confident rules. Additionally, in order to resolve possible rules conflict, a new estimated rule weight is proposed specifically for big data problems. IFC-BD was evaluated in comparison to the state-of-the-art approaches of the fuzzy classification paradigm, considering interpretability, accuracy, and running time. The findings of the experiments revealed that the proposed algorithm was able to improve the explainability of fuzzy rule-based classifiers as well as their predictive performance.