One-against-all ensemble for multiclass pattern classification

Authors:
Tatt Hee Oong;Nor Ashidi Mat Isa
Affiliations:
Imaging and Intelligent Systems Research Team (ISRT), School of Electrical and Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, 14300 Nibong Tebal, Penang, Malaysia;Imaging and Intelligent Systems Research Team (ISRT), School of Electrical and Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, 14300 Nibong Tebal, Penang, Malaysia
Venue:
Applied Soft Computing
Year:
2012

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Abstract

This paper presents a new method called one-against-all ensemble for solving multiclass pattern classification problems. The proposed method incorporates a neural network ensemble into the one-against-all method to improve the generalization performance of the classifier. The experimental results show that the proposed method can reduce the uncertainty of the decision and it is comparable to the other widely used methods.