Fuzzy Bayesian Learning
This provides a method for fuzzy systems with Bayesian parameter estimation, which is incremental as it combines existing techniques (fuzzy inference, Bayesian methods, MCMC) in a novel way for specific applications.
The paper tackles the problem of learning from data using rule-based fuzzy inference systems by estimating model parameters with Bayesian inference and MCMC techniques, demonstrating applicability in regression, classification, and knowledge extraction tasks with synthetic and real-world financial data.
In this paper we propose a novel approach for learning from data using rule based fuzzy inference systems where the model parameters are estimated using Bayesian inference and Markov Chain Monte Carlo (MCMC) techniques. We show the applicability of the method for regression and classification tasks using synthetic data-sets and also a real world example in the financial services industry. Then we demonstrate how the method can be extended for knowledge extraction to select the individual rules in a Bayesian way which best explains the given data. Finally we discuss the advantages and pitfalls of using this method over state-of-the-art techniques and highlight the specific class of problems where this would be useful.