An improved online learning algorithm for general fuzzy min-max neural network
This work addresses incremental improvements for researchers and practitioners using fuzzy min-max neural networks in classification tasks.
The paper tackled issues in the online learning algorithm for general fuzzy min-max neural networks, such as expansion/contraction steps and handling unseen data on decision boundaries, resulting in improved classification accuracy and stability compared to the original method and other classifiers.
This paper proposes an improved version of the current online learning algorithm for a general fuzzy min-max neural network (GFMM) 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.