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![0_image_0.png](0_image_0.png) 

(noise).

A second trend in tree size is apparent in the results presented in Table 5: as the noise level increases the size of the trees formed from unltered data grows more quickly than the size of the trees formed from ltered data. This reinforces the well-known phenomenon that noise in the class labels increases the size of a decision tree. An exception to this general trend is observed for the re severity dataset. Note that for this dataset, accuracy and tree size are approximately constant across various noise levels. This results because for each level of noise the method throws out almost the same subset of the instances.

## 4.6 Voting Versus Filtering

A hypothesis of interest is whether a ma jority vote ensemble classier can be used instead of ltering. To test this hypothesis we formed two ma jority vote ensemble classiers: one from the ltered and one from unltered data. The ma jority vote ensemble serves as the nal classier and not as the lter (as shown in the bottom two schemes depicted in Figure 10). The resulting classiers were then used to classify the uncorrupted test data.

The results for the land cover data are shown in Table 6. For each of three methods

(None, Ma jority and Consensus) we compare the accuracy of a ma jority vote classier to the 1-NN classier, which is the most accurate of the three base-level classiers for this domain.

The table includes the p-values of a paired t-test to assess the signicance in the dierence found between the ma jority classier and 1-NN classier. The ma jority vote classier is made up of a 1-NN, a decision tree and a linear machine. We did not use any weighting scheme for combining their votes. For each ltering scheme, the ma jority vote classier has equal or better accuracy than the 1-NN classier with the exception of noise levels of 0-20%

for the ma jority lter. At lower noise levels (0-10%) ltering does not have a large impact on the accuracy of the ma jority vote classier. However, at higher noise levels (20-30%),

both ma jority and consensus ltering improve the ma jority vote classier's accuracy over that obtained when no ltering method was applied.

