AUC optimization vs. error rate minimization, In: NIPS, pp.313-320, 2003. ,
SMOTE: synthetic minority over-sampling technique, JAIR, pp.321-357, 2002. ,
SMOTE-RSB *: a hybrid preprocessing approach based on oversampling and undersampling for high imbalanced data-sets using SMOTE and rough sets theory, Knowledge and Information Systems, vol.18, issue.1, pp.245-265, 2012. ,
DOI : 10.1109/TKDE.2006.17
SMOTEBoost: Improving Prediction of the Minority Class in Boosting, pp.107-119, 2003. ,
DOI : 10.1007/978-3-540-39804-2_12
RUSBoost: A Hybrid Approach to Alleviating Class Imbalance, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, vol.40, issue.1, pp.185-197, 2010. ,
DOI : 10.1109/TSMCA.2009.2029559
Adacost: Misclassification costsensitive boosting, In: ICML, pp.97-105, 1999. ,
When is Undersampling Effective in Unbalanced Classification Tasks?, pp.200-215, 2015. ,
DOI : 10.1007/978-3-319-23528-8_13
Predicting good probabilities with supervised learning, Proceedings of the 22nd international conference on Machine learning , ICML '05, pp.625-632, 2005. ,
DOI : 10.1145/1102351.1102430
Calibrating Probability with Undersampling for Unbalanced Classification, 2015 IEEE Symposium Series on Computational Intelligence, pp.159-166, 2015. ,
DOI : 10.1109/SSCI.2015.33
Learning to Rank for Information Retrieval, 2011. ,
Optimizing search engines using clickthrough data, Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '02, pp.133-142, 2002. ,
DOI : 10.1145/775047.775067
A support vector method for optimizing average precision, Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR '07, pp.271-278, 2007. ,
DOI : 10.1145/1277741.1277790
Random forests, Machine Learning, vol.45, issue.1, pp.5-32, 2001. ,
DOI : 10.1023/A:1010933404324
A short introduction to boosting, Journal- Japanese Society For Artificial Intelligence, vol.14, pp.771-780, 1999. ,
Yahoo! learning to rank challenge overview, pp.1-24, 2011. ,
Greedy function approximation: a gradient boosting machine, Annals of statistics, pp.1189-1232, 2001. ,
An efficient boosting algorithm for combining preferences, The Journal of machine learning research, vol.4, pp.933-969, 2003. ,
Learning to rank using gradient descent, Proceedings of the 22nd international conference on Machine learning , ICML '05, pp.89-96, 2005. ,
DOI : 10.1145/1102351.1102363
Optimising area under the ROC curve using gradient descent, Twenty-first international conference on Machine learning , ICML '04, p.49, 2004. ,
DOI : 10.1145/1015330.1015366
From ranknet to lambdarank to lambdamart: An overview, Learning, vol.11, pp.23-581, 2010. ,
AdaRank, Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR '07, pp.391-398, 2007. ,
DOI : 10.1145/1277741.1277809
Adapting boosting for information retrieval measures, Information Retrieval, vol.10, issue.3, pp.254-270, 2010. ,
DOI : 10.1007/s10791-009-9112-1
Learning to rank with nonsmooth cost functions, pp.193-200, 2007. ,
The meaning and use of the area under a receiver operating characteristic (ROC) curve., Radiology, vol.143, issue.1, pp.29-36, 1982. ,
DOI : 10.1148/radiology.143.1.7063747
Stochastic gradient boosting, Computational Statistics & Data Analysis, vol.38, issue.4, pp.367-378, 2002. ,
DOI : 10.1016/S0167-9473(01)00065-2
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.31.1666
Top rank optimization in linear time, In: NIPS, pp.1502-1510, 2014. ,