Obtaining Optimal Class Distribution for Decision Trees: Comparative Analysis of CTC and C4.5

Author:

Albisua Iñaki,Arbelaitz Olatz,Gurrutxaga Ibai,Martín José I.,Muguerza Javier,Pérez Jesús M.,Perona Iñigo

Publisher

Springer Berlin Heidelberg

Reference17 articles.

1. Asuncion, A., Newman, D.J.: UCI Machine Learning Repository University of California, School of Information and Computer Science, Irvine (2007), http://www.ics.uci.edu/~mlearn/MLRepository.html

2. Chawla, N.V., Bowyer, K.W., Kegelmeyer, W.P.: SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research 16, 321–357 (2002)

3. Chawla, N.V.: C4.5 and Imbalanced Data sets: Investigating the effect of sampling method, probabilistic estimate, and decision tree structure. In: Proc. of the Workshop on Learning from Imbalanced Data Sets, ICML, Washington DC (2003)

4. Demšar, J.: Statistical Comparisons of Classifiers over Multiple Data Sets. Journal of Machine Learning Research 7, 1–30 (2006)

5. Drummond, C., Holte, R.C.: Exploiting the Cost (In)sensitivity of Decision Tree Splitting Criteria. In: Proc. of the 17th Int. Conf. on Machine Learning, pp. 239–246 (2000)

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