Cost-sensitive learning is an aspect of algorithm-level modifications for class imbalance. Here, instead of using a standard error-driven evaluation (or 0–1 loss function), a misclassification cost is being introduced in order to minimize the conditional risk....
CITATION STYLE
Fernández, A., García, S., Galar, M., Prati, R. C., Krawczyk, B., & Herrera, F. (2018). Cost-Sensitive Learning. In Learning from Imbalanced Data Sets (pp. 63–78). Springer International Publishing. https://doi.org/10.1007/978-3-319-98074-4_4
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