An Improved Online Learning Algorithm for General Fuzzy Min-Max Neural Network

Publication Type:
Conference Proceeding
Proceedings of the International Joint Conference on Neural Networks, 2020, 00, pp. 1-9
Issue Date:
Filename Description Size
09207534.pdf1.61 MB
Adobe PDF
Full metadata record
© 2020 IEEE. This paper proposes an improved version of the current online learning algorithm for a general fuzzy min-max (GFMM) neural network to tackle existing issues concerning expansion and contraction steps as well as the way of dealing with unseen data located on decision boundaries. These drawbacks lower its classification performance, so an improved algorithm is proposed in this study to address the above limitations. The proposed approach does not use the contraction process for overlapping hyperboxes, which is more likely to increase the error rate as shown in the literature. The empirical results indicated the improvement in the classification accuracy and stability of the proposed method compared to the original version and other fuzzy min-max classifiers. In order to reduce the sensitivity to the training samples presentation order of this new on-line learning algorithm, a simple ensemble method is also proposed.
Please use this identifier to cite or link to this item: