Abstract

While implementing any machine learning algorithms it is good to have the descriptive knowledge of the dataset. In any dataset, in case having more than 90% of the data in target variable is from class 1 and the remaining data is from class 2. In such type of dataset, error evaluation metric accuracy is not going to help much. Having the unknown dataset with only class 1 itself gives more than 90% accuracy, which shows accuracy as evaluation metric should be ignored. Such a problem with highly skewed target outcome is known as an Imbalanced classification problem. There is a number of techniques to deal with imbalanced dataset. In this paper, we are interested to see how sampling techniques and XGBoost can be used while working with the Imbalanced dataset.

References

• Cochran, W.G. (1977). Sampling Techniques. New York: Wiley.
• Richard G. Lyons, How Fast Must You Sample? , Test and Measurement World, November, 1988, pp. 47-57.
• N. V. Chawla, D. A. Cieslak, L. O. Hall, and A. Joshi, “Automatically countering imbalance and its empirical relationship to cost,” Data Mining and Knowledge Discovery, vol. 17, no. 2, pp. 225–252, 2008.
• H. Han, W. Y. Wang, and B. H. Mao, “Borderline-smote: A new over-sampling method in imbalanced data sets learning,” in Advances in Intelligent Computing, (Hefei, China), vol. 3644, pp. 878–887, Springer-Verlag, 2005.
• N. Japkowicz “Learning from imbalanced data sets: A comparison of various strategies,” in AAAI Workshop on Learning from Imbalanced Data Sets, (Austin, Texas), vol. 68, AAAI Press, 2000.
• Almuallim H., An Efficient Algorithm for Optimal Pruning of Decision Trees. Artificial Intelligence 83(2): 347-362, 1996.
• Breiman L., Friedman J., Olshen R., and Stone C.. Classification and Regression Trees. Wadsworth Int. Group, 1984
• Trevor Hastie, Rob Tibshirani, Jerome Friedman (2009) “Statistical Learning” (Springer).
• Leo Breiman (2001) “Random Forests” Machine Learning, 45, 5-32.
• Bradley, A.P., 1997. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recogn. 30 (7), 1145–1159
• Saitta, L., Neri, F., 1998. Learning in the ‘‘real world’’. Mach. Learning 30, 133–163.
• Egan, J.P., 1975. Signal detection theory and ROC analysis, Series in Cognition and Perception. Academic Press, New York.
• Zhou, Z.-H. (2012). Ensemble Methods: Foundations and Algorithms. Chapman & Hall/CRC, 1st edition.
• Schapire, R. E., and Freund, Y. (2012). Boosting: Foundations and Algorithms. The MIT Press.