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Comparing Classification Results between N-ary and Binary Problems

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Quality Measures in Data Mining

Part of the book series: Studies in Computational Intelligence ((SCI,volume 43))

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References

  1. Hanley J. A. and McNeil B. J. The meaning and use of the area under a receiver operating characteristic (roc) curve. Radiology, 143:29-36, 1982.

    Google Scholar 

  2. Brors B. and Warnat P. Comparisons of machine learning algorithms on different microarray data sets. A tutorial of the Computational Oncology Group, Div. Theoretical Bioinformatics, German Cancer Research Center, 2004.

    Google Scholar 

  3. Olshen R. Breiman L., Friedman J. and Stone C. Classification and regression trees. Wadsworth International Group, 1984.

    Google Scholar 

  4. Leite E. and Harper P. Scaling regression trees: Reducing the np-complete problem for binary grouping of regression tree splits to complexity of order n. Southampton University, 2005.

    Google Scholar 

  5. Hernandez-Orallo J. Ferri C., Flach P. Learning decision trees using the area under the roc curve. Proceedings of the Nineteenth International Conference on Machine Learning (ICML 2002), pages 139-146, 2002.

    Google Scholar 

  6. Kass G. An exploratory technique for investigating large quantities of categorical data. Applied Statistics, 29:119-127, 1980.

    Article  Google Scholar 

  7. Kononenko I. and Bratko I. Information-based evaluation criterion for classifier’s performance. Machine Learning, 6:67-80, 1991.

    Google Scholar 

  8. Suykens J. A. K. and Vanderwalle J. Least square support vector machine classifiers. Neural Processing Letters, 9:293-300, 1999.

    Article  Google Scholar 

  9. Bradley A. P. The use of the area under the roc curve in the evaluation of machine learning algorithms. Pattern Recognition, 30:1145-1159, 1997.

    Article  Google Scholar 

  10. Flach P. The geometry of roc space: Understanding machine learning metrics through roc isometrics. Proceedings of the twentieth International Conference on Machine Learning (ICML 2003), pages 194-201, 2003.

    Google Scholar 

  11. Quinlan R. Induction of decision trees. Machine Learning, 1:81-106, 1986.

    Google Scholar 

  12. Quinlan R. C4.5: Programs for Machine Learning. Morgan Kaufmann, 1993.

    Google Scholar 

  13. Gammerman A. Saunders C. and Vovk V. Ridge regression learning algorithm in dual variables. Proceedings of the fifteenth International Conferencence on Machine Learning (ICML 1998), pages 515-521, 1998.

    Google Scholar 

  14. Bhattacharya P. Sindhwani V. and Rakshit S. Information theoretic feature crediting in multiclass support vector machines. Proceedings of the first SIAM International Conference on Data Mining, 2001.

    Google Scholar 

  15. Tsamardinos I. Hardin D. Statnikov A., Aliferis C. F. and Levy S. A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis. Bioinformatics, 25:631-643, 2005.

    Google Scholar 

  16. Diettrich T. and Bakiri G. Solving multiclass learning problems via errorcorrecting output codes. Journal of Artificial Intelligence Research, 2:263-286, 1995.

    Google Scholar 

  17. M. H. Zweig and G. Campbell. Receiver operating characteristic (roc) plots. Clinical Chemistry, 29:561-577, 1993.

    Google Scholar 

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Felkin, M. (2007). Comparing Classification Results between N-ary and Binary Problems. In: Guillet, F.J., Hamilton, H.J. (eds) Quality Measures in Data Mining. Studies in Computational Intelligence, vol 43. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-44918-8_12

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  • DOI: https://doi.org/10.1007/978-3-540-44918-8_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44911-9

  • Online ISBN: 978-3-540-44918-8

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