Utilizing non-uniform cost learning for active control of inter-class confusion

In this paper, we demonstrate the use of learning with non-uniform error-cost as a novel technique to design a multiclass cost-sensitive classifier. We investigate two important aspects of the design. First, we show that the learning is effective enough for active control of the multiclass confusion matrix using the cost-matrix. Second, we study the cases when the classifiers have mild model mismatch problems, and conclude that our design still have better performance compared to the conventional cost-sensitive classifier design.