Using AdaBoost-based Multiple Functional Neural Fuzzy Classifiers Fusion for Classification Applications
- Publication Type:
- Conference Proceeding
- MATEC Web of Conferences, 2018, 201
- Issue Date:
© The Authors, published by EDP Sciences, 2018. In this study, two intelligent classifiers, the AdaBoost-based incremental functional neural fuzzy classifier (AIFNFC) and the AdaBoost-based fixed functional neural fuzzy classifier (AFFNFC), are proposed for solving the classification problems. The AIFNFC approach will increase the amount of functional neural fuzzy classifiers based on the corresponding error during the training phase; while the AFNFC approach is equipped with a fixed amount of functional neural fuzzy classifiers. Then, the weights of AdaBoost procedure are assigned for classifiers. The proposed methods are applied to different classification benchmarks. Results of this study demonstrate the effectiveness of the proposed AIFNFC and AFFNFC methods.
Please use this identifier to cite or link to this item: