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Handling class imbalance in customer behavior prediction
2014
2014 International Conference on Collaboration Technologies and Systems (CTS)
Class imbalance is a common problem in real world applications and it affects significantly the prediction accuracy. In this study, investigation on better handling class imbalance problem in customer behavior prediction is performed. Using a more appropriate evaluation metric (AUC), we investigated the increase of performance for under-sampling and two machine learning algorithms (weight Random Forests and RUSBoost) against a benchmark case of just using Random Forests. Results show that
doi:10.1109/cts.2014.6867549
dblp:conf/cts/LiuWAA14
fatcat:siijogd6dzdxtem3l7fmscva4y