[1] Sonak and R. A. Patankar, “A Survey on Methods to Handle Imbalance Dataset,” International Journal of Computer Science and Mobile Computing, vol. 4, no. 11, pp. 338–343, 2015. [Online].Available:http://ijcsmc.com/docs/papers/November2015/ V4I11201573.pdf
[2] Singh and A. Purohit, “A survey on methods for solving data imbalance problem for classification,” International Journal of Computer Applications, vol. 127, no. 15, pp. 37–41, 2015.
[3] N.Chawla, K. Bowyer, L. Hall, and W. Kegelmeyer, “SMOTE: Synthetic Minority Over-sampling Technique,” Jair, vol. 16, pp.321–357, 2002.
[4] More, “Survey of resampling techniques for improving classification performance in unbalanced datasets,” vol. 10000, pp.1–7, 2016. [Online]. Available: http://arxiv.org/abs/1608.06048
[5] Z. Zheng, X. Wu, and R. Srihari, “Feature selection for text categorization on imbalanced data,” ACM Sigkdd Explorations Newsletter, vol. 6, no. 1, pp. 80–89, 2004.
[6] M. Wasikowski and X. W. Chen, “Combating the small sample class imbalance problem using feature selection,” IEEE Transactions
[7] Guyon and A. Elisseeff, “An introduction to variable and feature selection,” Journal of machine learning research, vol. 3, no.Mar, pp. 1157–1182, 2003.
[8] M. Alibeigi, S. Hashemi, and A. Hamzeh, “Dbfs: An effective density based feature selection scheme for small sample size and high dimensional imbalanced data sets,” Data & Knowledge Engineering, vol. 81, pp. 67–103, 2012.
[9] F. Sebastiani, “Machine learning in automated text categorization,” ACM computing surveys (CSUR), vol. 34, no. 1, pp. 1–47,2002.
[10] M. Alibeigi, S. Hashemi, and A. Hamzeh, “DBFS: An effective Density Based Feature Selection scheme for small sample size and high dimensional imbalanced data sets,” Data and Knowledge Engineering, vol. 81-82, pp. 67–103, 2012. [Online].Available: http://dx.doi.org/10.1016/j.datak.2012.08.001
[11] N. V. Chawla, N. Japkowicz, and A. Kotcz, “Special issue on learning from imbalanced data sets,” ACM Sigkdd Explorations Newsletter, vol. 6, no. 1, pp. 1–6, 2004.
[12] H. He and E. A. Garcia, “Learning from imbalanced data,” IEEE Transactions on Knowledge & Data Engineering, no. 9, pp.1263–1284, 2008.
[13] X.-w. Chen and M. Wasikowski, “Fast: a roc-based feature selection metric for small samples and imbalanced data classification problems,” in Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM,2008, pp. 124–132.
[14] G. Forman, “An extensive empirical study of feature selection metrics for text classification,” Journal of machine learning research, vol. 3, no. Mar, pp. 1289–1305, 2003.
[15] H. Pant and R. Srivastava, “A Survey on Feature Selection Methods For Imbalanced Datasets,” International Journal of Computer Engineering and Applications, vol. 9, no. 2, pp. 197–204, 2015.
[16] L. Breiman, "Bagging Predictors," Machine learning, vol. 24, no. 2, pp. 123–140, 1996.
[17] Y. Freund and R. E. Schapire, “A decision-theoretic generalization of on-line learning and an application to boosting,” Journal of computer and system sciences, vol. 55, no. 1, pp. 119–139, 1997.
[18] R. E. Schapire, “The strength of weak learnability,” Machine learning, vol. 5, no. 2, pp. 197–227, 1990.
[19] X. Wu, V. Kumar, J. R. Quinlan, J. Ghosh, Q. Yang, H. Motoda, G. J. McLachlan, A. Ng, B. Liu, S. Y. Philip et al., “Top 10 algorithms in data mining,” Knowledge and information systems, vol. 14, no. 1, pp. 1–37, 2008.
[20] J. Friedman, T. Hastie, R. Tibshirani et al., “Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors),” The annals of statistics, vol. 28, no. 2, pp. 337–407, 2000.
[21] Rudin, I. Daubechies, and R. E. Schapire, "The dynamics of AdaBoost: Cyclic behavior and convergence of margins," Journal of Machine Learning Research, vol. 5, no. Dec, pp. 1557–1595, 2004.
[22] K. Veropoulos, C. Campbell, N. Cristianini et al., “Controlling the sensitivity of support vector machines,” in Proceedings of the international joint conference on AI, vol. 55, 1999, p. 60.
[23] 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.
[24] N. V. Chawla, K.W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, "Smote: synthetic minority over-sampling technique,” Journal of artificial intelligence research, vol. 16, pp. 321–357, 2002.
[25] Seiffert, T. M. Khoshgoftaar, J. Van Hulse, and A. Napolitano, “Rusboost: A hybrid approach to alleviating class imbalance,”
[26] Q. Wang, “A hybrid sampling SVM approach to imbalanced data classification," in Abstract and Applied Analysis, vol. 2014.Hindawi, 2014.
[27] G. E. Batista, R. C. Prati, and M. C. Monard, “A study of the behavior of several methods for balancing machine learning training data,” ACM SIGKDD explorations newsletter, vol. 6, no. 1, pp. 20–29, 2004.
[28] M. Galar, A. Fernandez, E. Barrenechea, H. Bustince, and F. Herrera, "A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches," IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 42, no. 4, pp. 463–484, 2012.
[29] D.K. Mittal, V. Verma, R. Rastogi, "A Comparative Study and New Model for Smart Mirror", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.6, pp.58-61, 2017
[30] Dharmendra Sharma and Suresh Jain, "Evaluation of Stemming and Stop Word Techniques on Text Classification Problem", International Journal of Scientific Research in Computer Science and Engineering, Vol.3, Issue.2, pp.1-4, 2015
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