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Design Ensemble Machine Learning Model for Breast Cancer Diagnosis

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Abstract

In this paper, we classify the breast cancer of medical diagnostic data. Information gain has been adapted for feature selections. Neural fuzzy (NF), k-nearest neighbor (KNN), quadratic classifier (QC), each single model scheme as well as their associated, ensemble ones have been developed for classifications. In addition, a combined ensemble model with these three schemes has been constructed for further validations. The experimental results indicate that the ensemble learning performs better than individual single ones. Moreover, the combined ensemble model illustrates the highest accuracy of classifications for the breast cancer among all models.

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References

  1. Thomas, G. D., Ensemble methods in machine learning. In Proc. of the First International Workshop on Multiple Classifier System (MCS 2000), 1–15, 2000.

  2. Tsymbal, A., Pechenizkiy, M., and Cunningham, P., Diversity in search strategies for ensemble feature selection. Inform. Fusion 6:83–98, 2005.

    Article  Google Scholar 

  3. Xing, E. P., et al., Feature selection for high-dimensional genomic microarray data. In ICML’01: Proceedings of the Eighteenth International Conference on Machine Learning, 601–608. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 2001.

  4. Mitchell, T., Machine learning. McGraw-Hill, New York, 1997.

    MATH  Google Scholar 

  5. Yao, X., and Liu, Y., A new evolutionary system for evolving artificial neural networks. IEEE Trans. Neural Netw. 8:694–713, 1997.

    Article  Google Scholar 

  6. Wang, Z., “Neuro-Fuzzy ensemble approach for microarray cancer gene expression data analysis,” 2006 International Symposium on Evolving Fuzzy Systems, September, 2006.

  7. Ding, C. H. Q., and Peng, H., Minimum redundancy feature selection from microarray gene expression data. In CSB, 523–529, 2003.

  8. UC Irvine Machine Learning Repository http://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Original%29

  9. Mangasarian, O. L., and Wolberg, W. H., Cancer diagnosis via linear programming. SIAM News 23(5):1–18, 1990.

    Google Scholar 

  10. Quinlan, J. R., Improved use of continuous attributes in C4.5. J. Artif. Intell. Res. 4:7–90, 1996.

    Google Scholar 

  11. Sharkey, A. J. C., Sharkey, N. E., et al., Adapting an ensemble approach for the diagnosis of breast cancer. Proceedings of ICANN, 1998.

  12. Abonyi, J., and Szeifert, F., Supervised fuzzy clustering for the identification of fuzzy classifiers. Pattern Recogn. Lett. 24:2195–2207, 2003.

    Article  MATH  Google Scholar 

  13. Martínez-Muñoz, G., and Suárez, A., Switching class labels to generate classification ensembles. Pattern Recogn. 38:1483–1494, 2005.

    Article  Google Scholar 

  14. Şahan, S., Polat, K., Kodaz, H., et al., A new hybrid method based on fuzzy-artificial immune system and k-nn algorithm for breast cancer diagnosis. Comput. Biol. Med. 37(3):415–423, 2007.

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Network and Computer Centre, National Chiao Tung University, Hsinchu, Taiwan

    Sheau-Ling Hsieh & I-Shun Lee

  2. Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan

    Feipei Lai

  3. Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan

    Feipei Lai

  4. Network and Computer Centre, National Taiwan University, Taipei, Taiwan

    Chi-Huang Chen & Kai-Ping Hsu

  5. Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan

    Feipei Lai

  6. Computing Laboratory, Oxford University, Oxford, UK

    Zhenyu Wang

  7. Department of Software Engineering, National Kaohsiung Normal University, Kaohsiung, Taiwan

    Po-Hsun Cheng

  8. Department of Computer Science and Information Engineering, Providence University, Taichung, Taiwan

    Sung-Huai Hsieh

Authors
  1. Sheau-Ling Hsieh
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  2. Sung-Huai Hsieh
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  3. Po-Hsun Cheng
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  4. Chi-Huang Chen
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  5. Kai-Ping Hsu
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  6. I-Shun Lee
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  7. Zhenyu Wang
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  8. Feipei Lai
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Correspondence to Po-Hsun Cheng.

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Hsieh, SL., Hsieh, SH., Cheng, PH. et al. Design Ensemble Machine Learning Model for Breast Cancer Diagnosis. J Med Syst 36, 2841–2847 (2012). https://doi.org/10.1007/s10916-011-9762-6

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  • DOI: https://doi.org/10.1007/s10916-011-9762-6

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