Cost-sensitive machine learning[1][2] is an approach within machine learning that considers varying costs associated with different types of errors. This method diverges from traditional approaches by introducing a cost matrix, explicitly specifying the penalties or benefits for each type of prediction error. The inherent difficulty which cost-sensitive machine learning tackles is that minimizing different kinds of classification errors is a multi-objective optimization problem.
- ^ Ling, Charles X., and Victor S. Sheng. "Cost-sensitive learning and the class imbalance problem." Encyclopedia of machine learning 2011 (2008): 231-235. pdf
- ^ Cite error: The named reference
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