Advertisement

Journal of Classification

, Volume 25, Issue 1, pp 87–112 | Cite as

Mining Supervised Classification Performance Studies: A Meta-Analytic Investigation

  • Adrien JamainEmail author
  • David J. Hand
Article

Abstract

There have been many comparative studies of classification methods in which real datasets are used as a gauge to assess the relative performance of the methods. Since these comparisons often yield inconclusive or limited results on how methods perform, it is often believed that a broader approach combining these studies would shed some light on this difficult question. This paper describes such an attempt: we have sampled the available literature and created a dataset of 5807 classification results. We show that one of the possible ways to analyze the resulting data is an overall assessment of the classification methods, and we present methods for that particular aim. The merits and demerits of such an approach are discussed, and conclusions are drawn which may assist future research: we argue that the current state of the literature hardly allows large-scale investigations.

Keywords

Classification rules Supervised classification Neural networks Tree classifiers Logistic regression Nearest neighbor method Bradley-Terry Meta-analysis Data mining 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. BATCHELOR, B. G. and HAND, D. J. (1976), “A Pattern Recognition Competition”, in Proceedings of the Third International Joint Conference on Pattern Recognition, San Diego, 1976.Google Scholar
  2. BERTHOLD, M.R. and DIAMOND, J. (1998), “Constructive Training of Probabilistic Neural Networks”, Neurocomputing, 19:167–183.CrossRefGoogle Scholar
  3. BLUE, J.L., CANDELA, G.T., GROTHER, P.J., CHELLAPPA, R., and WILSON, C.L. (1994), “Evaluation of Pattern Classifiers for Fingerprint and OCR Applications”, Pattern Recognition, 4:485–501.CrossRefGoogle Scholar
  4. BRADLEY, R.A. and TERRY, M.E. (1952), “Rank Analysis of Incomplete Block Designs: I. The Method of Paired Comparisons”, Biometrika, 39:324–345.zbMATHMathSciNetGoogle Scholar
  5. BRAZDIL, P.B., SOARES, C., and PINTO DA COSTA, J. (2003), “Ranking Learning Algorithms: Using IBL and Meta-Learning on Accuracy and Time Results”, Machine Learning, 50:251–277.zbMATHCrossRefGoogle Scholar
  6. COLLETT, D. (2002), Modelling Binary Data (2nd ed.), London: Chapman and Hall.Google Scholar
  7. CURRAM, S.P. and MINGERS, J. (1994), “Neural Networks, Decision Tree Induction and Discriminant Analysis”, Journal of Operational Research Society, 45:440–450.zbMATHCrossRefGoogle Scholar
  8. DIETTERICH, T.G. (2000), “An Experimental Comparison of Three Methods for Constructing Ensembles of Decisions Trees: Bagging, Boosting, and Randomization”, Machine Learning, 40:139–157.CrossRefGoogle Scholar
  9. DUIN, R.P.W. (1996), “A Note on Comparing Classifiers”, Pattern Recognition Letters, 17:529–536.CrossRefGoogle Scholar
  10. EKLUND, P.W. and HOANG, A. (2002), “A Performance Survey of Public Domain Supervised Machine Learning Algorithms”, http://citeseer.nj.nec.com/142129.html.
  11. FISHER, R.A. (1936), “The Use of Multiple Measurements in Taxonomic Problems”, Annals of Eugenics, 7:179–188.Google Scholar
  12. FUKUNAGA, K. (1990), Introduction to Statistical Pattern Recognition, San Diego: Academic Press.zbMATHGoogle Scholar
  13. HAND, D.J. (2004), “Academic Obsessions and Classification Realities: Ignoring Practicalities in Supervised Classification”, in Classification, Clustering and Data Mining Applications, eds. B. Banks, L. House, F. R. McMorris, P. Arabie, and W. Gaul, Berlin: Springer, pp. 209–232.Google Scholar
  14. HAND, D.J. (1981), Discrimination and Classification, Chichester: Wiley.zbMATHGoogle Scholar
  15. HAND, D.J. (1997), Construction and Assessment of Classification Rules, Chichester: Wiley.zbMATHGoogle Scholar
  16. HAND, D.J., MANNILA, H., and SMYTH, P. (2001), Principles of Data Mining, Cambridge MA: MIT Press.Google Scholar
  17. HASTIE, T., TIBSHIRANI, R., and FRIEDMAN, J. (2001), The Elements of Statistical Learning Theory, New York: Springer.Google Scholar
  18. HOOPER, P.M. (1999), “Reference Point Logistic Classification”, Journal of Classification, 16(1):91–116.zbMATHCrossRefMathSciNetGoogle Scholar
  19. JAMAIN, A. (2004), “Meta-analysis of Classification Methods”, PhD thesis, Department of Mathematics, Imperial College, London.Google Scholar
  20. JAMAIN, A. and HAND, D.J. (2005), “The Naive Bayes Mystery: a Classification Detective Story”, Pattern Recognition Letters, 26:1752–1760.CrossRefGoogle Scholar
  21. KLEINBERG, E.M. (2000), “On the Algorithmic Implementation of Stochastic Discrimination”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(5): 473–490.CrossRefGoogle Scholar
  22. LIM, T., LOH, W., and SHIH, Y. (2000), “A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-Three Old and New Classification Algorithms”, Machine Learning, 40:203–228.zbMATHCrossRefGoogle Scholar
  23. MCLACHLAN, G.J. (1992), Discriminant Analysis and Statistical Pattern Recognition, New York: Wiley.Google Scholar
  24. METAL CONSORTIUM (2002), “Esprit Project METAL (#26.357)”, http://www.metalkdd.org.
  25. MICHIE, D., SPIEGELHALTER, D.J., and TAYLOR, C.C. (1994), Machine Learning, Neural and Statistical Classification, New York: Ellis Horwood. zbMATHGoogle Scholar
  26. MITCHELL, T.M. (1997), Machine Learning, New York: McGraw-Hill.zbMATHGoogle Scholar
  27. RASMUSSEN, C.E., NEAL, R.M., HINTON, G.E., VAN CAMP, D., REVOW, M., GHAHRAMANI, Z., KUSTRA, R., and TIBSHIRANI, R. (1996), “DELVE, Data for Evaluating Learning in Valid Experiments”, http://www.cs.toronto.edu/~delve/.
  28. RENDELL, L. and SESHU, R. (1990), “Learning Hard Concepts Through Constructive Induction”, Computational Intelligence, 6:247–270.CrossRefGoogle Scholar
  29. RIPLEY, B.D. (1996), Pattern Recognition and Neural Networks, Cambridge: Cambridge University Press.zbMATHGoogle Scholar
  30. SARGENT, D.J. (2001), “Comparison of Artificial Neural Networks with Other Statistical Approaches”, Cancer, 91:1636–42.CrossRefGoogle Scholar
  31. SCHIAVO, R.A. and HAND, D.J. (2000), “Ten More Years of Error Rate Research”, International Statistical Review, 68(3):295–310.zbMATHCrossRefGoogle Scholar
  32. SOHN, S.Y. (1999), “Meta-analysis of Classification Algorithms for Pattern Recognition”, IEEE Transactions on Pattern Recognition and Machine Intelligence, 21(11):1137–1144.CrossRefGoogle Scholar
  33. VAN DER LINDEN, W.J. and HAMBLETON, R.K. (1997), Handbook of Modern Item Response Theory, New York: Springer-Verlag.zbMATHGoogle Scholar
  34. WEBB, A. (2002), Statistical Pattern Recognition (2nd ed.), London: Arnold.zbMATHGoogle Scholar
  35. ZARNDT, F. (1995), “A Comprehensive Case Study: an Examination of Machine Learning and Connectionnist Algorithms”, http://citeseer.nj.nec.com/481595.html.

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  1. 1.BNP-ParibasLondonUK
  2. 2.Department of Mathematics, and Institute for Mathematical SciencesImperial CollegeLondonUK

Personalised recommendations