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- W4386960695 abstract "Credit card fraud is one of the most common crimes in online transactions, posing a significant threat to banks, credit card companies, and consumers. There are several strategies to prevent and identify these anomalous activities, but they all struggle with the class imbalance problem. Usually, traditional algorithms tend to ignore this unequal distribution of data, thereby neglecting the misclassification of costs. This leads to poor classification, which affects the fraud prediction. Generic data level algorithms are typically used to balance such unequal data, but cost misclassification (Singh and Jain in J Inf Optim Sci 41:1–14, 2020 [1]) persists, so this paper attempts to solve both the class imbalance problem (Singh and Jain in J Inf Optim Sci 41:1–14, 2020 [1]; Singh et al. in J Exper Theor Artif Intell 34:571–598, 2021 [2]) and the cost misclassification problem by using a correlation-based feature selection method, optimizing these features with the help of an optimization algorithm, the Genetic algorithm (GA) (Roseline et al. in Comput Electr Eng 102:1–11, 2022 [3]), and finally, a cost-sensitive (Ling et al. in Cost sensitive learning. Encyclopedia of machine learning. Springer, Boston, pp 231–235, 2011 [4]) classifier to deal with cost misclassification." @default.
- W4386960695 created "2023-09-23" @default.
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- W4386960695 date "2023-01-01" @default.
- W4386960695 modified "2023-09-29" @default.
- W4386960695 title "Curbing Anomalous Transactions Using Cost-Sensitive Learning" @default.
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- W4386960695 doi "https://doi.org/10.1007/978-981-99-3758-5_53" @default.
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