Multi-class Classification with One-Against-One Using Probabilistic Extreme Learning Machine
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.
KeywordsExtreme learning machine probabilistic extreme learning machine Binary classification Wastewater treatment
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