ARCHIVES
Review Article
Evaluating Data Mining Algorithms
Amit S. Bharti1
Vipul L. Borkar2
Bhagyashree Kumbhare3
Yamini B. Laxane4
12Students, MCA, Smt. Radhikatai Pandav College of Engineering, Nagpur, Maharashtra, India. 3Professor, MCA, Smt. Radhikatai Pandav College of Engineering, Nagpur, Maharashtra, India. 4HOD, MCA, Smt. Radhikatai Pandav College of Engineering, Nagpur, Maharashtra, India.
Published Online: January-April 2025
Pages: 79-82
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20250401013
References
1. Breiman, L., Friedman, J., Stone, C. J., & Olshen, R. A. (1984). Classification and regression trees. CRC press.
2. Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3), 273-297.
3. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press
4. Han, J., Pei, J., & Kamber, M. (2011). Data mining: concepts and techniques. Elsevier.
5. Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: data mining, inference, and prediction. Springer
Science & Business Media.
6. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
7. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., ... & Duchesnay, E. (2011). Scikit-learn: Machine learning
in Python. Journal of machine learning research, 12(Oct), 2825-2830.
8. Quinlan, J. R. (1986). Induction of decision trees. Machine learning, 1(1), 81-106.
9. Vapnik, V. (1999). The nature of statistical learning theory. Springer science & business media.
10. Zhang, G. P. (2000). Neural networks for classification: a survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C
(Applications and Reviews), 30(4), 451-462.
11. Bishop, C. M. (2006). Pattern recognition and machine learning. Springer.
12. Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. Proceedings of the 22nd acm sigkdd international conference on
knowledge discovery and data mining (pp. 785-794).
13. Dua, D., & Graff, C. (2017). UCI Machine Learning Repository. University of California, Irvine, School of Information and Computer
Sciences. [http://archive.ics.uci.edu/ml]
14. Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: synthetic minority over-sampling technique. Journal of
artificial intelligence research, 16, 321-357.
15. Demšar, J. (2006). Statistical comparisons of classifiers over multiple data sets. The Journal of Machine Learning Research, 7, 1-30.
2. Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3), 273-297.
3. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press
4. Han, J., Pei, J., & Kamber, M. (2011). Data mining: concepts and techniques. Elsevier.
5. Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: data mining, inference, and prediction. Springer
Science & Business Media.
6. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
7. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., ... & Duchesnay, E. (2011). Scikit-learn: Machine learning
in Python. Journal of machine learning research, 12(Oct), 2825-2830.
8. Quinlan, J. R. (1986). Induction of decision trees. Machine learning, 1(1), 81-106.
9. Vapnik, V. (1999). The nature of statistical learning theory. Springer science & business media.
10. Zhang, G. P. (2000). Neural networks for classification: a survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C
(Applications and Reviews), 30(4), 451-462.
11. Bishop, C. M. (2006). Pattern recognition and machine learning. Springer.
12. Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. Proceedings of the 22nd acm sigkdd international conference on
knowledge discovery and data mining (pp. 785-794).
13. Dua, D., & Graff, C. (2017). UCI Machine Learning Repository. University of California, Irvine, School of Information and Computer
Sciences. [http://archive.ics.uci.edu/ml]
14. Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: synthetic minority over-sampling technique. Journal of
artificial intelligence research, 16, 321-357.
15. Demšar, J. (2006). Statistical comparisons of classifiers over multiple data sets. The Journal of Machine Learning Research, 7, 1-30.
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