Self-organizing fuzzy inference ensemble system for big streaming data classification

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 deciency, 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 in- ference 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 de- cision boundaries based on the inter-class and intra-class distances between pro- totypes identied from successive data chunks for higher classication precision. Thanks to its parallel distributed computing architecture, the proposed ensem- ble framework can achieve great classication precision while maintain high computational eciency 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 classication accuracy and computational eciency.