Voting based weighted online sequential extreme learning machine for imbalance multi-class classification
Bilal Mirza, Zhiping Lin, Jiuwen Cao, Xiaoping Lai · 2015
In this paper, a voting based weighted online sequential extreme learning machine (VWOS-ELM) is proposed for class imbalance learning (CIL). VWOS-ELM is the first sequential classifier that can tackle the class imbalance problem in multi-class data streams. Utilizing WOS-ELM and the recently proposed voting based online sequential extreme learning machine (VOS-ELM) method, VWOS-ELM adapts better to newly received data than the original WOS-ELM method. Experimental results show that VWOS-ELM outperforms both the WOS-ELM and the recent meta-cognitive extreme learning machine methods. It also achieves similar performance to that of ensemble of subset OS-ELM (ESOS-ELM) but using fewer independent classifiers.