@inproceedings{7881a1e9b026468dbde996ad9acca4df,
title = "Handling class imbalance in customer behavior prediction",
abstract = "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 under-sampling is the most effective way to deal with class imbalance. RUSBoost, as a specific algorithm designed to deal with class imbalance problem, is also effective but not as good as under-sampling. Weighted Random Forests, as a cost-sensitive learner, only improves the performance of appetency classification problem out of three classification problems.",
keywords = "Class imbalance, Customer behavior, Prediction, Random forests, RUSBoost, Under-sampling",
author = "Nengbao Liu and Woon, {Wei Lee} and Zeyar Aung and Afshin Afshari",
year = "2014",
doi = "10.1109/CTS.2014.6867549",
language = "British English",
isbn = "9781479951567",
series = "2014 International Conference on Collaboration Technologies and Systems, CTS 2014",
publisher = "IEEE Computer Society",
pages = "100--103",
booktitle = "2014 International Conference on Collaboration Technologies and Systems, CTS 2014",
address = "United States",
note = "2014 15th International Conference on Collaboration Technologies and Systems, CTS 2014 ; Conference date: 19-05-2014 Through 23-05-2014",
}