Minimax Classifier for Uncertain Costs
This addresses the challenge of applying classifiers in varying real-world scenarios with uncertain costs, such as different expert opinions, though it appears incremental as it builds on existing cost-sensitive learning frameworks.
The study tackled the problem of cost-sensitive learning when multiple cost matrices are present by proposing a minimax classifier, theoretically proving that the problem reduces to solving standard cost-sensitive problems and sub-problems with only two matrices, and validating this with preliminary empirical results.
Many studies on the cost-sensitive learning assumed that a unique cost matrix is known for a problem. However, this assumption may not hold for many real-world problems. For example, a classifier might need to be applied in several circumstances, each of which associates with a different cost matrix. Or, different human experts have different opinions about the costs for a given problem. Motivated by these facts, this study aims to seek the minimax classifier over multiple cost matrices. In summary, we theoretically proved that, no matter how many cost matrices are involved, the minimax problem can be tackled by solving a number of standard cost-sensitive problems and sub-problems that involve only two cost matrices. As a result, a general framework for achieving minimax classifier over multiple cost matrices is suggested and justified by preliminary empirical studies.