An evolving intelligent system (EIS) is able to self-update its system structure and meta-parameters from streaming data. However, since the majority of EISs are implemented on a single-model architecture, their performances on large-scale, complex data streams are often limited. To address this deficiency, a novel self-organizing fuzzy inference ensemble framework is proposed in this paper. As the base learner of the proposed ensemble system, the self-organizing fuzzy inference system is capable of self-learning a highly transparent predictive model from streaming data on a chunk-by-chunk basis through a human-interpretable process. Very importantly, the base learner can continuously self-adjust its decision boundaries based on the inter-class and intra-class distances between prototypes identified from successive data chunks for higher classification precision. Thanks to its parallel distributed computing architecture, the proposed ensemble framework can achieve great classification precision while maintain high computational efficiency on large-scale problems. Numerical examples based on popular benchmark big data problems demonstrate the superior performance of the proposed approach over the state-of-the-art alternatives in terms of both classification accuracy and computational efficiency.

不断发展的智能系统（EIS）能够从流数据中自动更新其系统结构和元参数。但是，由于大多数EIS是在单一模型体系结构上实现的，因此它们在大规模，复杂数据流上的性能通常受到限制。针对这一不足，提出了一种新颖的自组织模糊推理集成框架。作为提出的集成系统的基础学习者，自组织模糊推理系统能够通过人类可解释的过程，从逐块的流数据中自学习高度透明的预测模型。非常重要的是 基础学习者可以基于从连续数据块中识别出的原型之间的类间和类内距离，连续不断地自我调整其决策边界，以实现更高的分类精度。由于其并行的分布式计算体系结构，因此所提出的集成框架可以在实现大型分类问题的同时，保持较高的计算效率，同时实现较高的分类精度。基于流行的基准大数据问题的数值示例证明了该方法在分类准确性和计算效率方面均优于最新的替代方法。提出的集成框架可以在实现大规模分类的同时，保持较高的计算效率，同时实现较高的分类精度。基于流行的基准大数据问题的数值示例证明了该方法在分类准确性和计算效率方面均优于最新的替代方法。提出的集成框架可以在实现大规模分类的同时，保持较高的计算效率，同时实现较高的分类精度。基于流行的基准大数据问题的数值示例证明了该方法在分类准确性和计算效率方面均优于最新的替代方法。