Using AdaBoost-based Multiple Functional Neural Fuzzy Classifiers Fusion for Classification Applications

Jyun Yu Jhang, Chin Ling Lee, Cheng Jian Lin*, Chin Teng Lin, Kuu-Young Young

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

Abstract

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.

Original languageEnglish
Article number05004
JournalMATEC Web of Conferences
Volume201
DOIs
StatePublished - 14 Sep 2018
Event3rd International Conference on Inventions, ICI 2017 - Sun Moon Lake, Taiwan
Duration: 29 Sep 20172 Oct 2017

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