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.