Motivated by the practical demands for simplification of data toward 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 article proposes a method to construct classifiers from multiresolution hierarchical granular representations 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 a high accuracy in comparison to other fuzzy min-max models 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 handle 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 analyses 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.

中文翻译：

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使用通用模糊最小-最大神经网络的有效多分辨率分层粒度表示分类器


在数据简化以符合人类思维和问题解决以及对不确定性的容忍的实际需求的推动下，信息颗粒正在成为不同数据抽象级别的数据处理中的重要实体。本文提出了一种使用超盒模糊集从多分辨率分层粒度表示构造分类器的方法。所提出的方法通过多个抽象级别分层​​形成一系列粒度推理。我们的分类器的一个吸引人的特点是，与其他模糊最小-最大模型相比，它可以基于重用从较低抽象级别学到的知识，在低粒度上保持较高的准确性。此外，我们的方法可以显着减少数据大小，并处理与实际应用程序中的数据相关的不确定性和不完整性。分类器的构建过程包括两个阶段。第一阶段是在最大粒度上制定模型，而后期的目标是降低所构建模型的复杂性，并从更高抽象级别的数据中进行推导。对合成数据集和真实数据集进行的全面实验分析表明，与其他类型的模糊最小最大神经网络和常见机器学习算法相比，我们的方法在训练时间和预测性能方面的效率。