Abstract
This study proposes a stacking fuzzy classifier with stochastic configuration-based learning that can achieve higher training and testing performances and sound interpretability of fuzzy rules. By using the understandable first-order Takagi–Sugeno–Kang fuzzy system, we initially stack each successive subclassifier on both the remaining misclassified training data and the corresponding outputs of the previous subclassifier. Subsequently, a Stacking Fuzzy Classifier with Fully Interpretable and Short fuzzy Rules (FISR-SFC) further improves its prediction by linearly aggregating the outputs of all the subclassifiers. FISR-SFC trains each subclassifier using the proposed stochastic configuration-based learning procedure to utilize its training excellence on gradually smaller misclassified training data and simultaneously maintain the full interpretability of each subclassifier. Experimental results on twelve benchmarking datasets reveal that FISR-SFC is at least comparable to and even better than the comparative classifiers in terms of average testing accuracy/G-mean and/or short rules with full interpretability.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.