Summary
In pattern recognition, many learning methods need numbers as inputs. This paper analyzes two-level classifier ensembles to improve numeric learning methods on nominal data. A different classifier was used at each level. The classifier at the base level transforms the nominal inputs into continuous probabilities that the classifier at the meta level uses as inputs. An experimental validation is provided over 27 nominal datasets for enhancing a method that requires numerical inputs (e.g. Support Vector Machine, SVM). Cascading, Stacking and Grading are used as two-level ensemble implementations. Experiments combine these methods with another symbolic-to-numerical transformation — Value Difference Metric, VDM. The results suggest that Cascading with Binary Decision Trees at base level and SVM with VDM at meta level produces a better accuracy than other possible two-level configurations.
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References
Grabczewski K, Jankowski N (2003) Transformations of symbolic data for continuous data oriented models. In: Kaynak O, Alpaydin E, Oja E, Xu L (eds) Proc Artif Neural Networks and Neural Inf Proc, Istanbul, Turkey. Springer, Berlin/Heidelberg, pp 359–366
Stanfill C, Waltz D (1986) Toward memory-based reasoning. Communications of the ACM 29:1213–1229
Duch W, Grudzinski K, Stawski G (2000) Symbolic features in neural networks. In: Rutkowski L, Cze¸stochowa R (eds) Proc the 5th Conf Neural Networks and Soft Computing, Zakopane, Poland, pp 180–185
Gama J, Brazdil P (2000) Cascade generalization. Mach Learn 41:315–343
Maudes J, Rodríguez JJ, García-Osorio S (2007) Cascading for nominal data. In: Haindl M, Kittler J, Roli F (eds) Proc the 7th Int Workshop on Multiple Classifier Syst, Prague, Czech Republic. Springer, Berlin/Heidelberg, pp 231–240
Wolpert D (1992) Stacked generalization. Neural Networks 5:241–260
Seewald AK, Fürnkranz J (2001) An evaluation of grading classifiers. In: Hoffmann F, Hand DJ, Adams NM, Fisher DH, Guimarães G (eds) Proc the 4th Int Conf Advances in Intell Data Analysis, Cascais, Portugal. Springer, Berlin/Heidelberg, pp 115–124
Witten IH, Frank E (2005) Data mining: practical machine learning tools and techniques. Morgan Kaufmann, San Francisco
Platt J (1999) Fast training of support vector machines using sequential minimal optimization. In: Schölkopf B, Burges C, Smola A (eds) Advances in kernel methods, MIT Press, Cambridge, pp 185–208
Nadeau C, Bengio Y (2003) Inference for the generalization error. Mach Learn 52:239–281
Blake CL, Merz CJ (1998) UCI Repository of machine learning databases (http://www.ics.uci.edu/~mlearn/MLRepository.html)
Demšar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Research 7:1–30
Quinlan JR (1993) C4.5: programs for machine learning. Morgan Kaufmann, San Francisco
Kohavi R, Wolpert D (1996) Bias plus variance decomposition for zero-one loss functions. In: Saitta L (ed) Proc the 13th Int Conf Mach Learn, Bari, Italy. Morgan Kaufmann, San Francisco, pp 275–283
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Maudes, J., Rodríguez, J.J., García-Osorio, C. (2008). Cascading with VDM and Binary Decision Trees for Nominal Data. In: Okun, O., Valentini, G. (eds) Supervised and Unsupervised Ensemble Methods and their Applications. Studies in Computational Intelligence, vol 126. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78981-9_9
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DOI: https://doi.org/10.1007/978-3-540-78981-9_9
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