the method to eliminate many good instances  we will expand this point when we discuss the error rates of the filters in Section and   show the results of the road segmentation and scene segmentation Fig ures respectively  These two datasets are similar in that they were labeled using the same visual labeling process described in Section         but differ in the features measured and the set of classes  For the road segmentation data  all three filtering methods perform comparably and in each case substantially improve accuracy relative to not filtering  For this dataset using a decision tree as a final classifier yielded slightly better performance than a   NN and we show results for this method  For the scene segmentation data a   NN was a slightly better final classifier as shown in Table For this dataset at noise levels of and the majority filter performs better than each of the others and retains base line accuracy at noise The improvement in accuracy from filtering for each of these two segmentation datasets can be attributed to their class separability Specifically it is relatively easy to spot outliers because three of the features in each dataset measure color  and many of the classes are well separated in spectral space

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The results for the fire seventy dataset differ substantially from the other four datasets Figure shows the results for the decision tree algorithm across the six noise levels For willing the control noise  Recall that noise was introduced among classes On but not in class On closer investigation we discovered that when applying filtering to the original dataset      added noise  almost all of the instances from classes were tagged as noisy by all three filter methods Table shows a comparison of the distribution of instances in the original dataset and of the instances left after a majority filter has been applied  We examined the misclassification matrix for the original data and found that about half of the errors resulted from classifying instances as class and the other half were from classifying class x as class y where labeled x / y and
≤ x, y ≤     After filtering  the dataset contains a preponderance of instances Such an uneven distribution of classes results in classifiers biased belonging to class toward classifying every instance as class which in the original distribution of the instances maintained in the uncorrupted test instances  is roughly Int roducing more noise into did not change this behavior and therefore the accuracy curves  see Figure classes