{"id":{"repo_id":"reykjavik","oai_identifier":"oai:skemman.is:1946/34915"},"canonical_url":"https://search.dev.ndltd.org/etd/reykjavik/oai:skemman.is:1946/34915","repository":{"repo_id":"reykjavik","name":"Reykjavík University","base_url":"https://skemman.is/oai/request"},"display":{"title":"A replication of a cost-sensitive decision tree approach for fraud detection","abstract":"Payment card fraud poses a problem with widespread implications. However, the class imbalance between genuine and fraudulent transactions presents a challenge to traditional learning methods. Cost-based methods address this problem by assigning different costs to the misclassification of each class. Sahin et al. [7] define a cost-sensitive decision tree algorithm, and this study attempts to both replicate and build on their findings. The results presented here do not support the hypothesis that the cost-sensitive algorithm provides an improvement over traditional decision tree algorithms. 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However, the class imbalance between genuine and fraudulent transactions presents a challenge to traditional learning methods. Cost-based methods address this problem by assigning different costs to the misclassification of each class. Sahin et al. [7] define a cost-sensitive decision tree algorithm, and this study attempts to both replicate and build on their findings. The results presented here do not support the hypothesis that the cost-sensitive algorithm provides an improvement over traditional decision tree algorithms. However, the results do suggest that different methods of prioritizing alerted transactions can lead to performance improvements over some measures."]},{"key":"dc:title","label":"Title","values":["A replication of a cost-sensitive decision tree approach for fraud detection"]}]}],"canonical_facts":{"dc:contributor":["Háskólinn í Reykjavík"],"dc:creator":["Kristófer Reynisson 1995-"],"dc:date.accessioned":["2020-01-21T11:13:34Z"],"dc:date.available":["2020-01-21T11:13:34Z"],"dc:date.issued":["2020-01-21T11:13:34Z"],"dc:description.abstract":["Payment card fraud poses a problem with widespread implications. However, the class imbalance between genuine and fraudulent transactions presents a challenge to traditional learning methods. Cost-based methods address this problem by assigning different costs to the misclassification of each class. Sahin et al. [7] define a cost-sensitive decision tree algorithm, and this study attempts to both replicate and build on their findings. 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