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›› 2015, Vol. 30 ›› Issue (4): 874-887.

• Special Section on Data Management and Data Mining •

### Classifying Uncertain and Evolving Data Streams with Distributed Extreme Learning Machine

Dong-Hong Han1,2(韩东红), Member, CCF, Xin Zhang1(张昕), Guo-Ren Wang1,2(王国仁), Senior Member, CCF

1. 1. College of Information Science and Engineering, Northeastern University, Shenyang 110819, China;
2. Key Laboratory of Medical Image Computing (NEU), Ministry of Education, Shenyang 110819, China
• Received:2015-01-31 Revised:2015-05-15 Online:2015-07-05 Published:2015-07-05
• About author:Dong-Hong Han received her M.S. and Ph.D. degrees in computer science and technology from Northeastern University, Shenyang, in 2002 and 2007, respectively. Currently, she is an associate professor in the College of Information Science and Engineering, Northeastern University, Shenyang. Her research interests include data stream management, data mining, and uncertain data management.
• Supported by:

This work was supported by the National Natural Science Foundation of China under Grant Nos. 61173029 and 61272182.

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 efficient and accurate classifiers for uncertain data streams. In this paper, we first introduce Distributed Extreme Learning 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 efficiently 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 learned using DELM. The weights of the base classifiers are updated dynamically according to classification results. WE-DELM improves both the efficiency in learning the model and the accuracy in performing classification. Experimental results show that WE-DELM achieves better performance on different evaluation criteria, including efficiency, accuracy, and speedup.

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