Multi-class classification with one-against-one using probabilistic extreme learning machine

Authors:
Li-jie Zhao;Tian-you Chai;Xiao-kun Diao;De-cheng Yuan
Affiliations:
State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, China,College of Information Engineering, Shenyang University of Chemical Technology, Shen ...;State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, China;College of Information Engineering, Shenyang University of Chemical Technology, Shenyang, China;College of Information Engineering, Shenyang University of Chemical Technology, Shenyang, China
Venue:
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
Year:
2012

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Abstract

Probabilistic extreme learning machine (PELM) is a binary classification method, which can improve the computational speed, generalization performance and computational cost. In this work we extend the binary PELM to resolve multi-class classification problems by using one-against-one (OAO) and winner-takes-all strategy. The strategy one-against-one (OAO) involves C(C-1)/2 binary PELM models. A reliability for each sample is calculated from each binary PELM model, and the sample is assigned to the class with the largest combined reliability by using the winner-takes-all strategy. The proposed method is verified with the operational conditions classification of an industrial wastewater treatment plant. Experimental results show the good performance on classification accuracy and computational expense.