Multiple compensatory neural fuzzy networks fusion using fuzzy integral
Chi-Yung Lee, Cheng‐Jian Lin · 2007
This paper presents a novel method for combining multiple compensatory neural fuzzy networks (CNFN) using fuzzy integral. The fusion of multiple classifiers can overcome the limitations of a single classifier since the classifiers complement each other. A fuzzy integral is a better combination scheme than majority voting method that uses the subjectively defined relevance of classifiers. A combination of multiple CNFN classifiers with fuzzy integral (FI) is proposed to achieve data classification with higher accuracy than existing traditional methods. The advantage of the proposed method is that not only are the classification results combined but the relative importance of the different networks is also considered. Experimental results show that the fusion of multiple CNFNs using fuzzy integral can perform better than existing traditional methods.