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Decay-weighted extreme learning machine for balance and optimization learning

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Abstract

The original extreme learning machine (ELM) was designed for the balanced data, and it balanced misclassification cost of every sample to get the solution. Weighted extreme learning machine assumed that the balance can be achieved through the equality of misclassification costs. This paper improves previous weighted ELM with decay-weight matrix setting for balance and optimization learning. The decay-weight matrix is based on the sample number of each class, but the weight sum values of each class are not necessarily equal. When the number of samples is reduced, the weight sum is also reduced. By adjusting the decaying velocity, classifier could achieve more appropriate boundary position. From the experimental results, the decay-weighted ELM obtains the better effects in solving the imbalance classification tasks, particularly in multiclass tasks. This method was successfully applied to build the prediction model in the urban traffic congestion prediction system.

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Acknowledgements

This work was supported by National Nature Science Foundation of P. R. China (No. 61272357, 61300074) and National Key Research and Development Program of China (No. 2016YFB0700502, 2016YFB1001404).

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Authors and Affiliations

  1. University of Science and Technology Beijing, Beijing, 100083, China

    Qing Shen, Xiaojuan Ban, Ruoyi Liu & Yu Wang

  2. North Electronic Instrument Institute, Beijing, 100191, China

    Yu Wang

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  1. Qing Shen
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  2. Xiaojuan Ban
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  3. Ruoyi Liu
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  4. Yu Wang
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Correspondence to Qing Shen or Xiaojuan Ban.

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Shen, Q., Ban, X., Liu, R. et al. Decay-weighted extreme learning machine for balance and optimization learning. Machine Vision and Applications 28, 743–753 (2017). https://doi.org/10.1007/s00138-017-0828-4

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  • DOI: https://doi.org/10.1007/s00138-017-0828-4

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