POSSIBILISTIC METHODOLOGY FOR THE EVALUATION OF CLASSIFICATION ALGORITHMS

Olgierd Hryniewicz

Abstract

In the paper we consider the problem of the evaluation and comparison of different classification algorithms. For this purpose we apply the methodology of statistical tests for the multinomial distribution. We propose to use two-sample tests for the comparison of different classification algorithms, and one-sample goodness-of-fit tests for the evaluation of the quality of classification. We restrict our attention to the case of the supervised classification when an external ‘expert’ evaluates the correctness of classification. The results of the proposed statistical tests are interpreted using possibilistic indices of dominance introduced by Dubois and Prade.

References

  1. Agresti, A., 2006. Categorical Data Analysis. J. Wiley, Hoboken, N J, 2nd edition.
  2. Berthold, M., Hand, D. J. (Eds.), 2007. Intelligent Data Analysis. An Introduction, Springer, Berlin, 2nd edition.
  3. Breiman, L., Friedman, J., Olshen, R, Stone, C., 1984. Classification and Regression Trees, CRC Press, Boca Raton, FL.
  4. Charytanowicz, M., Niewczas J., Kulczycki, P., Kowalski, P. A., Lukasik, S. Zak, S., 2010. A Complete Gradient Clustering Algorithm for Features Analysis of X-ray Images". In: Information Technologies in Biomedicine, E. Pietka, E. Kawa (Eds.), Springer-Verlag, BerlinHeidelberg, 2010, 15-24.
  5. Desu, M. M., Raghavarao, D., 2004. Nonparametric Statistical Methods for Complete and Censored Data, Chapman & Hall, Boca Raton, FL.
  6. Dubois D., Prade, H., 1983. Ranking Fuzzy Numbers in the Setting of Possibility Theory. Information Science 30, 183-224.
  7. Gil, M. A., Hryniewicz, O., 2009. Statistics with Imprecise Data. In: Robert A. Meyers (Ed.): Encyclopedia of Complexity and Systems Science. Springer, Heidelberg, 8679-8690.
  8. Hryniewicz, O., 2000. Possibilistic Interpretation of the Results of Statistical Tests. Proceedings of Eight International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems IPMU 2000, Madrid, 215-219.
  9. Hryniewicz, O., 2006. Possibilistic decisions and fuzzy statistical tests. Fuzzy Sets and Systems, 157, 2665- 2673
  10. Krzanowski, W. J., 1988. Principles of Multivariate Analysis: A User's Perspective. Oxford University Press, New York.
  11. Kulczycki, P., Kowalski, P.A., 2011. Bayes classification of imprecise information of interval type. Control and Cybernetics 40 (in print)
  12. Nisbet, R., Elder, J., Miner, G., 2009. Statistical Analysis and Data Mining. Applications, Elsevier Inc, Amsterdam.
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Paper Citation


in Harvard Style

Hryniewicz O. (2011). POSSIBILISTIC METHODOLOGY FOR THE EVALUATION OF CLASSIFICATION ALGORITHMS . In Proceedings of the 6th International Conference on Software and Database Technologies - Volume 2: ICSOFT, ISBN 978-989-8425-77-5, pages 313-322. DOI: 10.5220/0003436803130322


in Bibtex Style

@conference{icsoft11,
author={Olgierd Hryniewicz},
title={POSSIBILISTIC METHODOLOGY FOR THE EVALUATION OF CLASSIFICATION ALGORITHMS},
booktitle={Proceedings of the 6th International Conference on Software and Database Technologies - Volume 2: ICSOFT,},
year={2011},
pages={313-322},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003436803130322},
isbn={978-989-8425-77-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Software and Database Technologies - Volume 2: ICSOFT,
TI - POSSIBILISTIC METHODOLOGY FOR THE EVALUATION OF CLASSIFICATION ALGORITHMS
SN - 978-989-8425-77-5
AU - Hryniewicz O.
PY - 2011
SP - 313
EP - 322
DO - 10.5220/0003436803130322