This paper proposes a hybrid architecture based on neural networks, fuzzy systems, and n-uninorms for solving pattern classification problems, termed as ENFS-Uni0 (short for evolving neuro-fuzzy system based on uni-nullneurons). The model can produce knowledge in an on-line (single-pass) and evolving learning context in a particular form of neuro-fuzzy rules representing the dependencies among input features through IF-THEN type relations. The rules antecedents are thereby realized through uni-nullneurons, which are constructed from n-uninorms, leading to the possibility to express both, AND- and OR-connections (and a mixture of these) among the single antecedent parts of a rule (and thus achieving an advanced interpretability aspect of the rules). The neurons’ evolution is done through an extended version of an autonomous data partition method (ADPA). On-line interpretation of the timely evolution of rules is addressed by (i) a concept for tracking the degree of changes of the rules over data stream samples, which may indicate experts/operators how much dynamics is in the process and may be used as a structural active learning component to request operator’s feedback in the case of significant changes and (ii) a concept for updating feature weights incrementally. These weights express the (possibly changing) impact degrees of features on the classification problem: features with low weights can be seen as unimportant and masked out when showing rules to an expert ($\to$ rule length reduction). The rules’ consequents are represented by certainty vectors and are recursively updated by an indicator-based recursive weighted least squares (I-RWLS) approach (one RWLS estimator per class) where the weights are given through the neuron activation levels in order to gain stable local learning. The model proposed in this paper was successfully compared to related hybrid and evolving approaches in the literature for classifying binary and multi-class patterns. The results obtained by the proposed model show an outperformance of the related works in terms of higher accuracy trend lines over time, while offering a high degree of interpretability through coherent neuro-fuzzy rules to solve the classification problems.

本文提出了一种基于神经网络，模糊系统和n-范数的混合架构，用于解决模式分类问题，称为ENFS-Uni0（用于基于单神经元的进化神经模糊系统的缩写）。该模型可以通过特定形式的神经模糊规则在在线（单遍）和不断发展的学习环境中产生知识，这些规则通过IF-THEN类型关系表示输入特征之间的依赖关系。规则的先例是通过单零神经元来实现的，单神经元是由n个标准构成的，从而有可能在规则的单个先例部分之间表达AND连接和OR连接（以及它们的混合）。从而实现了规则的高级可解释性。神经元的进化是通过自主数据分区方法（ADPA）的扩展版本完成的。（i）用于跟踪规则在数据流样本上的变化程度的概念解决了规则的及时演化的在线解释，该概念可以指示专家/操作员在此过程中有多少动态，并且可以用作一个结构性主动学习组件，可在发生重大变化的情况下请求操作员反馈；以及（ii）用于逐步更新特征权重的概念。这些权重表示要素对分类问题的影响程度（可能会发生变化）：权重低的要素在向专家展示规则时可以被视为不重要且被掩盖（它可以指示专家/操作员在过程中有多少动态，并且可以用作结构性主动学习组件，以在发生重大更改的情况下请求操作员反馈，以及（ii）用于增量更新特征权重的概念。这些权重表示要素对分类问题的影响程度（可能会发生变化）：权重低的要素在向专家展示规则时可以被视为不重要且被掩盖（它可以指示专家/操作员在过程中有多少动态，并且可以用作结构性主动学习组件，以在发生重大更改的情况下请求操作员反馈，以及（ii）用于增量更新特征权重的概念。这些权重表示要素对分类问题的影响程度（可能会发生变化）：权重低的要素在向专家展示规则时可以被视为不重要且被掩盖（$\to$规则长度减少）。规则的结果由确定性矢量表示，并通过基于指标的递归加权最小二乘（I-RWLS）方法（每类一个RWLS估计量）递归更新，其中权重通过神经元激活水平给出，以获得稳定本地学习。本文提出的模型已成功地与文献中相关的混合和进化方法进行了比较，以对二进制和多类模式进行分类。通过提出的模型获得的结果显示，随着时间的流逝，趋势线的准确性更高，相关作品的表现也不尽人意，同时通过相干的神经模糊规则来解决分类问题，从而提供了高度的可解释性。