Conventional classification algorithms are not well suited for the inherent uncertainty, potential concept drift, volume, and velocity of streaming data. Specialized algorithms are needed to obtain e?cient and accurate classifiers for uncertain data streams. In this paper, we first introduce Distributed Extreme Leing Machine (DELM), an optimization of ELM for large matrix operations over large datasets. We then present Weighted Ensemble Classifier Based on Distributed ELM (WE-DELM), an online and one-pass algorithm for e?ciently classifying uncertain streaming data with concept drift. A probability world model is built to transform uncertain streaming data into certain streaming data. Base classifiers are leed using DELM. The weights of the base classifiers are updated dynamically according to classification results. WE-DELM improves both the e?ciency in leing the model and the accuracy in performing classification. Experimental results show that WE-DELM achieves better performance on different evaluation criteria, including e?ciency, accuracy, and speedup.