Computer Science ›› 2017, Vol. 44 ›› Issue (4): 275-280.doi: 10.11896/j.issn.1002-137X.2017.04.057

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Improved Weighted Extreme Learning Machine

XING Sheng, WANG Xiao-lan, ZHAO Shi-xin and ZHAO Yan-xia   

  • Online:2018-11-13 Published:2018-11-13

Abstract: If the weight of the original weighted extreme learning machine is fixed artificially,the more optimal weight may be missed.Aiming at this problem,an improved weighted extreme learning machine was proposed.The new method uses the ratio of the sample number of different classes as the initial weight.The weight is adjusted by the weight adjustment factor from two directions of reducing and enlarging the weight ratio.Finally,the optimal weights are selected by the experimental results.The experiments were carried out on the transformed UCI data set using the original weighted extreme learning machine,the weighted extreme learning machine with other weights and the new method respectively.The experimental results indicate that the improved weighted extreme learning machine has better classification performance.

Key words: Imbalanced learning,Weighted extreme learning machine,Cost sensitive learning,Single hidden layer feedforward networks

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