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Multi-kernel learning for multivariate performance measures optimization

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Abstract

In this paper, we investigate the problem of optimizing complex multivariate performance measures to learn classifiers for pattern classification problems. For the first time, the multi-kernel learning is considered to construct a classifier to optimize a given nonlinear and non-smooth multivariate classifier performance measure. We estimate and optimize the upper bound of the given multivariate performance measure, instead of optimizing it directly. Moreover, to solve the problem of kernel function selection and kernel parameter tuning, we proposed to construct an optimal kernel by weighted linear combination of some candidate kernels. The learning of the classifier parameter and the kernel weight are unified in a single objective function considering minimizing the upper bound of the given multivariate performance measure. The objective function is optimized with regard to classifier parameter and kernel weight alternately in an iterative algorithm. The developed algorithm is evaluated on two different pattern classification methods with regard to various multivariate performance measure optimization problems. The experiment results show the proposed algorithm outperforms the competing methods.

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Authors and Affiliations

  1. Software School, Xiamen University, Xiamen, 361005, China

    Fan Lin & Jianbing Xiahou

  2. National Time Service Center, Chinese Academy of Sciences, Xi’an, 710600, China

    Jingbin Wang

  3. Graduate University of Chinese Academy of Sciences, Beijing, 100039, China

    Jingbin Wang

  4. Xiamen University of Technology, Xiamen, 361024, China

    Nian Zhang

  5. Department of Computer Science, Tulane University, New Orleans, LA, 70118, USA

    Nancy McDonald

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  1. Fan Lin
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  2. Jingbin Wang
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  3. Nian Zhang
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  4. Jianbing Xiahou
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  5. Nancy McDonald
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Correspondence to Jianbing Xiahou.

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Lin, F., Wang, J., Zhang, N. et al. Multi-kernel learning for multivariate performance measures optimization. Neural Comput & Applic 28, 2075–2087 (2017). https://doi.org/10.1007/s00521-015-2164-9

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