A meta classifier that makes its base classifier cost-sensitive. Two methods can be used to introduce cost-sensitivity: reweighting training instances according to the total cost assigned to each class; or predicting the class with minimum expected misclassification cost (rather than the most likely class). Performance can often be improved by using a bagged classifier to improve the probability estimates of the base classifier.

Since the classifier, in default mode (i.e., when using the reweighting method), normalizes the cost matrix before applying it, it can be hard coming up with a cost matrix, e.g., one that balances out imbalanced data. Here is an example:

  • input cost matrix
 -3  1  1
  1 -6  1
  0  0  0
  • normalized cost matrix
 0 7 1
 4 0 1
 3 6 0

The application of a cost matrix using the second, minimum-expected cost approach, which is also used by MetaCost, is more intuitive.

See also