Abstract
A method for applying weighted decoding to error-correcting output code ensembles of binary classifiers is presented. This method is sensitive to the target class in that a separate weight is computed for each base classifier and target class combination. Experiments on 11 UCI datasets show that the method tends to improve classification accuracy when using neural network or support vector machine base classifiers. It is further shown that weighted decoding combines well with the technique of bootstrapping to improve classification accuracy still further.
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References
Bishop, M.C.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)
Brown, G., Wyatt, J., Harris, R., Yao, X.: Diversity Creation Methods: A Survey and Categorisation. Journal of Information Fusion 6(1) (2005)
Burges, C.J.C.: A Tutorial on Support Vector Machines for Pattern Recognition. Knowledge Discovery and Data Mining 2(2) (1998)
Chang, C.-C., Lin, C.-J.: LIBSVM : a library for support vector machines (2001), http://www.csie.ntu.edu.tw/~cjlin/libsvm
Dietterich, T.G., Bakiri, G.: Solving Multiclass Learning Problems via Error-Correcting Output Codes. Journal of Artificial Intelligence Research 2, 263–286 (1995)
Duin, R.P.W., Juszczak, P., Paclik, P., Pekalska, E., de Ridder, D., Tax, D.M.J., Verzakov, S.: PRTools 4.1, A Matlab Toolbox for Pattern Recognition, Delft University of Technology (2007)
Efron, B., Tibshirani, R.J.: An Introduction to the Bootstrap. Chapman & Hall, Boca Raton (1993)
James, G.: Majority Vote Classifiers: Theory and Applications. PhD Dissertation, Stanford University (1998)
Merz, C.J., Murphy, P.M.: UCI Repository of Machine Learning Databases (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
Smith, R.S., Windeatt, T.: The Bias Variance Trade-off in Bootstrapped Error Correcting Output Code Ensembles. In: Benediktsson, J.A., Kittler, J., Roli, F. (eds.) MCS 2009. LNCS, vol. 5519, pp. 1–10. Springer, Heidelberg (2009)
Windeatt, T.: Accuracy/ Diversity and Ensemble Classifier Design. IEEE Trans. Neural Networks 17(4) (July 2006)
Windeatt, T., Smith, R.S., Dias, K.: Weighted Decoding ECOC for Facial Action Unit Classification. In: 18th European Conference on Artificial Intelligence (ECAI), Patras, Greece, July 2008, pp. 26–30 (2008)
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Smith, R.S., Windeatt, T. (2010). Class-Separability Weighting and Bootstrapping in Error Correcting Output Code Ensembles. In: El Gayar, N., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2010. Lecture Notes in Computer Science, vol 5997. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12127-2_19
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DOI: https://doi.org/10.1007/978-3-642-12127-2_19
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