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Multi-instance classification based on regularized multiple criteria linear programming

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Abstract

Regularized multiple criteria linear programming (RMCLP) model is a new powerful method for classification and has been used in various real-life data mining problems. In this paper, a new Multi-instance Classification method based on RMCLP was proposed (called MI-RMCLP), which includes two algorithms for linearly separable case and nonlinearly case separately. The key point of this method, instead of a mixed integer quadratic programming in MI-SVM, is that it is able to deal with multi-instance learning problem by an iterative strategy solving sequential quadratic programming problems. All experiment results have shown that MI-RMCLP method can converge to the optimal value in limited iterative steps and be a competitive method in multi-instance learning classification.

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Notes

  1. RBF kernel is defined by K(x,x′) = exp(− ||x − x '||2)/σ2.

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Acknowledgments

This work has been partially supported by grants from National Natural Science Foundation of China (NO.709-21061, NO.10601064), the CAS/SAFEA International Partnership Program for Creative Research Teams and Major International (Ragional) Joint Research Project (NO.71110107026), the President Fund of GUCAS, and the National Technology Support Program 2009BAH42B02.

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Authors and Affiliations

  1. Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing, 100190, China

    Zhiquan Qi, Yingjie Tian & Yong Shi

  2. College of Information Science and Technology, University of Nebraska at Omaha, Omaha, NE, 68182, USA

    Yong Shi

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  1. Zhiquan Qi
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  2. Yingjie Tian
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  3. Yong Shi
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Correspondence to Yingjie Tian or Yong Shi.

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Qi, Z., Tian, Y. & Shi, Y. Multi-instance classification based on regularized multiple criteria linear programming. Neural Comput & Applic 23, 857–863 (2013). https://doi.org/10.1007/s00521-012-1008-0

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  • DOI: https://doi.org/10.1007/s00521-012-1008-0

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