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Kernel-based Fuzzy-rough Nearest-neighbour Classification for Mammographic Risk Analysis

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Abstract

Mammographic risk analysis is an important task for assessing the likelihood of a woman developing breast cancer. It has attracted much attention in recent years as it can be used as an early risk indicator when screening patients. In this paper, a kernel-based fuzzy-rough nearest-neighbour approach to classification is employed to address the issue of the assessment of mammographic risk. Four different breast tissue density assessment metrics are employed to support this study, and the performance of the proposed approach is compared with alternative nearest-neighbour-based classifiers and other popular learning classification techniques. Systematic experimental results show that the work employed here generally improves the classification performance over the others, measured using criteria such as classification accuracy rate, root mean squared error and the kappa statistics. This demonstrates the potential of kernel-based fuzzy-rough nearest-neighbour classification as a robust and reliable tool for mammographic risk analysis.

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Notes

  1. The relevant WEKA software can be download from https://dl.dropboxusercontent.com/u/2043486/weka.jar.

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Acknowledgments

This work was partly supported by the National Natural Science Foundation of China (Grant No. 61272171), the Fundamental Research Funds for the Central Universities (Grant No. 3132014094, 3132013335, 3132013325) and the China Postdoctoral Science Foundation (Grant No. 2013M541213). The authors would like to thank the support provided by Aberystwyth University and by the colleagues in the Advanced Reasoning Group with the Department of Computer Science, Institute of Mathematics, Physics and Computer Science at Aberystwyth University, UK. The authors are also grateful to the reviewers for their constructive comments which have helped improve this work.

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Authors and Affiliations

  1. Department of Computer Science, Institute of Mathematics, Physics and Computer Science, Aberystwyth University, Wales, UK

    Changjing Shang, Qiang Shen & Neil Mac Parthaláin

  2. Information Science and Technology College, Dalian Maritime University, Dalian, People’s Republic of China

    Yanpeng Qu

  3. School of Mathematical Sciences, Dalian University of Technology, Dalian, People’s Republic of China

    Wei Wu

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  1. Yanpeng Qu
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  2. Changjing Shang
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  3. Qiang Shen
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  4. Neil Mac Parthaláin
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  5. Wei Wu
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Correspondence to Qiang Shen.

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Qu, Y., Shang, C., Shen, Q. et al. Kernel-based Fuzzy-rough Nearest-neighbour Classification for Mammographic Risk Analysis. Int. J. Fuzzy Syst. 17, 471–483 (2015). https://doi.org/10.1007/s40815-015-0044-1

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