Certain Bayesian Network based on Fuzzy knowledge Bases

TL;DR

This paper introduces fuzzy certain Bayesian networks (FCBN), combining Bayesian networks and fuzzy logic to better handle uncertainty and vagueness in decision-making processes.

Contribution

It proposes a novel framework that integrates fuzzy variables into certain Bayesian networks to improve robustness and decision quality under uncertainty.

Findings

Fuzzification enhances network robustness.

Fuzzy certain Bayesian networks better handle vagueness.

The approach improves decision-making under uncertainty.

Abstract

In this paper, we are trying to examine trade offs between fuzzy logic and certain Bayesian networks and we propose to combine their respective advantages into fuzzy certain Bayesian networks (FCBN), a certain Bayesian networks of fuzzy random variables. This paper deals with different definitions and classifications of uncertainty, sources of uncertainty, and theories and methodologies presented to deal with uncertainty. Fuzzification of crisp certainty degrees to fuzzy variables improves the quality of the network and tends to bring smoothness and robustness in the network performance. The aim is to provide a new approach for decision under uncertainty that combines three methodologies: Bayesian networks certainty distribution and fuzzy logic. Within the framework proposed in this paper, we address the issue of extending the certain networks to a fuzzy certain networks in order to cope with a vagueness and limitations of existing models for decision under imprecise and uncertain knowledge.