What are the mostly used, latest and effective techniques for learning from imbalancd dataset?
The techniques I am aware of:
* Resampling Techniques:
* Throw away minority examples and switch to an anomaly detection framework
* At the algorithm level, or after it
* Construct an entirely new algorithm to perform well on imbalanced data.
Are there any other new/effective techniques to look at?
Dear Imran,
Kindly refer to the following papers:
1. He, H., & Garcia, E. A. (2009). Learning from imbalanced data. IEEE Transactions on knowledge and data engineering, 21(9), 1263-1284.
2. Chawla, N. V., Japkowicz, N., & Kotcz, A. (2004). Special issue on learning from imbalanced data sets. ACM Sigkdd Explorations Newsletter, 6(1), 1-6.
3. Han, H., Wang, W. Y., & Mao, B. H. (2005, August). Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning. In International Conference on Intelligent Computing (pp. 878-887). Springer, Berlin, Heidelberg.
4. Ertekin, S., Huang, J., Bottou, L., & Giles, L. (2007, November). Learning on the border: active learning in imbalanced data classification. In Proceedings of the sixteenth ACM conference on Conference on information and knowledge management (pp. 127-136). ACM.
5. Cohen, G., Hilario, M., Sax, H., Hugonnet, S., & Geissbuhler, A. (2006). Learning from imbalanced data in surveillance of nosocomial infection. Artificial intelligence in medicine, 37(1), 7-18.
6. Haixiang, G., Yijing, L., Shang, J., Mingyun, G., Yuanyue, H., & Bing, G. (2017). Learning from class-imbalanced data: Review of methods and applications. Expert Systems with Applications, 73, 220-239.
Please consider the sixth paper with higher priority.
Thanks,
Sobhan.
Dear Abdullah Al Imran,
Ensemble technique, as listed below , are able to hand the imbalanced data very-well:
I think these techniques refer to your last topic ("new algorithm to perform well on imbalanced data"). More details and algorithm can be found at: https://www.analyticsvidhya.com/blog/2017/03/imbalanced-classification-problem/
Dear Imran,
Kindly refer to the following papers:
1. He, H., & Garcia, E. A. (2009). Learning from imbalanced data. IEEE Transactions on knowledge and data engineering, 21(9), 1263-1284.
2. Chawla, N. V., Japkowicz, N., & Kotcz, A. (2004). Special issue on learning from imbalanced data sets. ACM Sigkdd Explorations Newsletter, 6(1), 1-6.
3. Han, H., Wang, W. Y., & Mao, B. H. (2005, August). Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning. In International Conference on Intelligent Computing (pp. 878-887). Springer, Berlin, Heidelberg.
4. Ertekin, S., Huang, J., Bottou, L., & Giles, L. (2007, November). Learning on the border: active learning in imbalanced data classification. In Proceedings of the sixteenth ACM conference on Conference on information and knowledge management (pp. 127-136). ACM.
5. Cohen, G., Hilario, M., Sax, H., Hugonnet, S., & Geissbuhler, A. (2006). Learning from imbalanced data in surveillance of nosocomial infection. Artificial intelligence in medicine, 37(1), 7-18.
6. Haixiang, G., Yijing, L., Shang, J., Mingyun, G., Yuanyue, H., & Bing, G. (2017). Learning from class-imbalanced data: Review of methods and applications. Expert Systems with Applications, 73, 220-239.
Please consider the sixth paper with higher priority.
Thanks,
Sobhan.
See, please, also :
Conference Paper Experimental Perspectives on Learning from Imbalanced Data
Consider a lot of useful numerical data.
Article A survey on addressing high-class imbalance in big data
Here's a simple technique. Learn the probability density of each class and (assuming the class proportions in the dataset are the same as in real world) compute the class prior from the dataset (otherwise from real world). Then, using the likelihood and class prior for each class, you can make a Naive Bayes decision on which class the new instance belongs to.
Dear Abdullah Al Imran, when you talk about costs and sensitivity ("at the algorithm level" part of your question), I think you're referring to cost-sensitive learning. Here's a good starting point for that area:
Elkan, C. (2001, August). The foundations of cost-sensitive learning. In International joint conference on artificial intelligence (Vol. 17, No. 1, pp. 973-978). Lawrence Erlbaum Associates Ltd.
Multilevel support vector machines work well with imbalanced classes
https://link.springer.com/article/10.1007%2Fs10994-019-05800-7
- Badges
- Science topic
- Similar topics
- Computer Science
- Data Mining