An Effective Multi-Resolution Hierarchical Granular Representation based Classifier using General Fuzzy Min-Max Neural Network
This work addresses the need for interpretable and efficient classifiers in real-world applications with uncertain or incomplete data, though it appears incremental as it builds on existing fuzzy min-max neural network approaches.
The paper tackles the problem of building classifiers that simplify data to align with human reasoning and handle uncertainty by proposing a multi-resolution hierarchical granular representation classifier using hyperbox fuzzy sets. The method achieves relatively high accuracy at low granularity and reduces data size, with experimental results showing efficiency in training time and predictive performance compared to other fuzzy min-max neural networks and common machine learning algorithms.
Motivated by the practical demands for simplification of data towards being consistent with human thinking and problem solving as well as tolerance of uncertainty, information granules are becoming important entities in data processing at different levels of data abstraction. This paper proposes a method to construct classifiers from multi-resolution hierarchical granular representations (MRHGRC) using hyperbox fuzzy sets. The proposed approach forms a series of granular inferences hierarchically through many levels of abstraction. An attractive characteristic of our classifier is that it can maintain relatively high accuracy at a low degree of granularity based on reusing the knowledge learned from lower levels of abstraction. In addition, our approach can reduce the data size significantly as well as handling the uncertainty and incompleteness associated with data in real-world applications. The construction process of the classifier consists of two phases. The first phase is to formulate the model at the greatest level of granularity, while the later stage aims to reduce the complexity of the constructed model and deduce it from data at higher abstraction levels. Experimental outcomes conducted comprehensively on both synthetic and real datasets indicated the efficiency of our method in terms of training time and predictive performance in comparison to other types of fuzzy min-max neural networks and common machine learning algorithms.