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Controlling and Visualizing the Precision-Recall Tradeoff for External Performance Indices

  • Blaise HanczarEmail author
  • Mohamed Nadif
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11051)

Abstract

In many machine learning problems, the performance of the results is measured by indices that often combine precision and recall. In this paper, we study the behavior of such indices in function of the tradeoff precision-recall. We present a new tool of performance visualization and analysis referred to the tradeoff space, which plots the performance index in function of the precision-recall tradeoff. We analyse the properties of this new space and show its advantages over the precision-recall space. Code related to this paper is available at: https://sites.google.com/site/bhanczarhomepage/prerec.

Keywords

Evaluation Precision-recall 

References

  1. 1.
    Albatineh, A.N., Niewiadomska-Bugaj, M.: Correcting Jaccard and other similarity indices for chance agreement in cluster analysis. Adv. Data Anal. Classif. 5(3), 179–200 (2011)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Bergmann, S., Ihmels, J., Barkai, N.: Iterative signature algorithm for the analysis of large-scale gene expression data. Phys. Rev. E Stat. Nonlin. Soft Matter. Phys. 67, 031902 (2003)CrossRefGoogle Scholar
  3. 3.
    Buckland, M., Gey, F.: The relationship between recall and precision. J. Am. Soc. Inform. Sci. 45, 12–19 (1994)CrossRefGoogle Scholar
  4. 4.
    Busygin, S., Prokopyev, O., Pardalos, P.: Biclustering in data mining. Comput. Oper. Res. 35(9), 2964–2987 (2008)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Cheng, Y., Church, G.M.: Biclustering of expression data. Proc. Int. Conf. Intell. Syst. Mol. Biol. 8, 93–103 (2000)Google Scholar
  6. 6.
    Datta, S., Datta, S.: Methods for evaluating clustering algorithms for gene expression data using a reference set of functional classes. BMC Bioinform. 7, 397 (2006)CrossRefGoogle Scholar
  7. 7.
    Davis, J., Goadrich, M.: The relationship between precision-recall and ROC curves. In: Proceedings of the 23rd International Conference on Machine Learning, ICML 2006, pp. 233–240 (2006)Google Scholar
  8. 8.
    Drummond, C., Holte, R.C.: Cost curves: an improved method for visualizing classifier performance. Mach. Learn. 65, 95–130 (2006)CrossRefGoogle Scholar
  9. 9.
    Flach, P.A.: The geometry of ROC space: understanding machine learning metrics through ROC isometrics. In: ICML, pp. 194–201 (2003)Google Scholar
  10. 10.
    Govaert, G., Nadif, M.: Co-clustering: Models, Algorithms and Applications. Wiley, Hoboken (2013)CrossRefGoogle Scholar
  11. 11.
    Hanczar, B., Nadif, M.: Ensemble methods for biclustering tasks. Pattern Recogn. 45(11), 3938–3949 (2012)CrossRefGoogle Scholar
  12. 12.
    Hanczar, B., Nadif, M.: Precision recall space to correct external indices for biclustering. In: International Conference on Machine Learning ICML, vol. 2, pp. 136–144 (2013)Google Scholar
  13. 13.
    Jain, A.K.: Data clustering: 50 years beyond k-means. Pattern Recogn. Lett. 31(8), 651–666 (2010)CrossRefGoogle Scholar
  14. 14.
    Lazzeroni, L., Owen, A.: Plaid models for gene expression data. Technical report, Stanford University (2000)Google Scholar
  15. 15.
    Madeira, S.C., Oliveira, A.L.: Biclustering algorithms for biological data analysis: a survey. IEEE/ACM Trans. Comput. Biol. Bioinform. 1(1), 24–45 (2004)CrossRefGoogle Scholar
  16. 16.
    Manning, C.D., Raghavan, P., Schtze, H.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2008)CrossRefGoogle Scholar
  17. 17.
    Salah, A., Nadif, M.: Directional co-clustering. Adv. Data Anal. Classif, 1–30 (2018)Google Scholar
  18. 18.
    Sokolova, M., Japkowicz, N., Szpakowicz, S.: Beyond accuracy, F-score and ROC: a family of discriminant measures for performance evaluation. In: Proceedings of the 19th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence, AI 2006, pp. 1015–1021 (2006)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.IBISCUniversity of Paris-Saclay, Univ. EvryEvryFrance
  2. 2.LIPADEUniversity of Paris DescartesParisFrance

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