  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.
