hand  the results illustrate that the probability of a consensus filter electing to keep bad data is larger than the majority vote filters for each noise level across all data sets Indeed for a noise level of the CF has a land cover c redit road fire chance of retaining mislabeled instances  Forthe majority filter  the scene and chance of making E and E errors is more equal Excluding the credit data at noise the probability of these errors never reaches above Considering that majority vote performs better than consensus filters for higher noise rates this shows that a consensus filters propensity toward making E errors retaining bad data hinders performance more than majority filters lesser ability to retain good data i e majority makes more E errors The fire dataset has a very different profile The probability of throwing out good data remains almost constant across the different noise levels  This is because for this dataset good" instances appear to have a high level of noise  On the other hand  the probability that a filter will retain bad data rises as the noise level increases If one has a lot of data then an elevated E error is probably less of a hindrance than an elevated E error i e throwing out good data when you have a lot is less costly than retaining bad data Of course one would like to insure that one is not throwing out ecceptions

## 5. Conclusions And Future Directions

This article presents a procedure for identifying mislabeled instances  The results of an em pirical evaluation demonstrated that filtering improves classification accuracy for datasets that possess labeling errors Filtering allowed accuracies near to the base line accuracy to be retained for noise levels up to for all datasets and up to for two datasets the road and scene segmentation datasets Our experiments show that as the noise level increases the ability of the method to retain the baseline accuracy decreases  Moreover as illustrated by the fire severity dataset if the method starts with data that is overly noisy it cannot form an accurate filter A comparison of voting to filtering illustrated that the majority vote classifier performed better than the individual classifiers  but that it cannot replace filtenng when data are noisy  Our results show that the best approach is to combine filtering and voting An evaluation of the precision of filtering illustrated that consensus filters are conservative in throwing away good data at the expense of keeping mislabeled data whereas for majority vote filters the probability of throwing out good data and the probability of retaining bad data are more even  Because majority vote filters perform bet ter on average than consensus filters this shows that retaining bad data hinders performance more than throwing out good data for these datasets  This trend is particularly important when one has an abundance of data The issue of determining whether or not to apply filtering to a given data set must be considered For the work described here the data were artificially corrupted Therefore the nature and magnitude of the labeling errors were known a priori   Unfortunately  this type of information is rarely known for most real world" applications In some situations it may be possible to use domain knowledge to estimate the amount of label noise in a dataset   For ssituations where this knowledge is not available the conservative nature of the consensus filter dictates that relatively few instances will be discarded for data sets with low levels of