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Abstract: Very large databases with skewed class distributions and non-uniform cost per error are not uncommon in real-world data mining tasks. One such task is credit card fraud detection: the number of fraudulent transactions is small compared to legitimate ones, the amount of financial loss for each fraudulent transaction depends on the transaction amount and other factors, and millions of transactions occur each day. We devised a multi-classifier meta-learning approach to address these three issues.... (Update)
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BibTeX entry: (Update)
P. Chan and S. Stolfo. Toward scalable learning with non-uniform class and cost distributions: A case study in credit card fraud detection. In Proc. Fourth Intl. Conf. Knowledge Discovery and Data Mining, pages 164--168, 1998. http://citeseer.ist.psu.edu/article/chan98toward.html More
@inproceedings{ chan98toward,
author = "Philip K. Chan and Salvatore J. Stolfo",
title = "Toward Scalable Learning with Non-Uniform Class and Cost Distributions: A Case Study in Credit Card Fraud Detection",
booktitle = "Knowledge Discovery and Data Mining",
pages = "164-168",
year = "1998",
url = "citeseer.ist.psu.edu/article/chan98toward.html" }
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