
| Noise Level   |      |    |    |    |    |    |    |
|---------------|------|----|----|----|----|----|----|
| Actual Noise  |      |    |    |    |    |    |    |
| NN            | None | H  | H  | H  | H  | H  | H  |
| SF            | H    | 士 | H  | 士 | H  | 士 |    |
| MF            | H    | 士 | H  | 士 | H  | 士 |    |
| CF            | H    | ±  | ±  | ±  | ±  | ±  |    |
| LM            | None | H  | H  | H  | H  | H  | H  |
| SF            | H    | H  | H  | H  |    |    |    |
| +             | -H   |    |    |    |    |    |    |
| MF            | H    | 士 | H  | 士 | H  | 士 |    |
| CF            | F    | +  | ±  | +  | ±  | H  |    |
| D Tree        | None | H  | H  | H  | 士 | H  | 士 |
| SF            | H    | H  | H  | -H | H  | H  |    |
| MF            | H    | 士 | H  | 士 | H  | 士 |    |
| CF            | ±    | +  | +  | ±  | ±  | ±  |    |

Vj,j / i g, Y
g Y
For the rare cases in which g; Y
g y Y an arbitrary decision is made ourimplementation of an LM chooses the smaller of i and j i n these cases To find the weights of the linear machine we use the thermal training rule  Brodley A recent modification to this procedure Brodley Utgoff addresses the problem that the weights found by this rule depend on the order in which the instances are presented  a poor ordering can lead to an inaccurate classifier  To minimize this problem  the thermal training procedure is applied ten times  using a different ordering for the instances each time This produces ten LM s each with a different set of weights The LM that maximizes the information gain ratio metric is then chosen

## 4.4  Effect Of Filtering On Classification  Accuracy

we show the accuracy for the land cover data of the classifiers formed by each of In Table the three algorithms tested using no filter  None   a single algorithm filter  SF  7  a majority vote filter MF and a consensus filter CF The first row reports the noise rate used to corrupt the data  Note that for this dataset the percentage of the entire training set that is corrupted for a noise rate of x will be less than x because only some pairs of classes are considered problematic The actual percentage of corrupted training data is reported in the second row of the table When no noise is introduced filtering did not make a significant difference for any of the methods on this dataset  Since the original data is not guaranteed to be noise free  we have no way to evaluate whether filtering improves the irue classification accuracy using the test data available here