

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

rremoving the training instances that are pruned by C
Quinlan Specifically for each leaf node in the pruned tree for which training instances are observed from more than one class Johns method eliminates those instances that are not from the majority class The tree is then rebuilt from the reduced set of training instances This process iterates until no further pruning can be done  The key difference between our method and John s is that our method uses a cross validation over the training data with one iteration whereas Johns method deals with the training examples directly and performs multiple iterations A second way to implement filtering is to construct a filter using one algorithm and to construct the final classifier using a different algorithm The assumption underlying this approach is that some algorithms act as good filters for other algorithms  much like some algorithms act as good feature selection methods for others  Cardie The approach described by Wilson to filtering data for a   NN using a k NN is an example of this aproach

## 3.2 Ensemble Filters

Ensemble classifiers combine the outputs of a set of base level classifiers Hansen Salamon Benediktsson   Swain Wolpert A majority vote ensemble classifier will to outperform each base level classifier on a dataset if two conditions hold the probability of a correct classification by each individual classifier is greater than and the errors in predictions of the base level classifiers are independent Hansen Salamon In filtering an ensemble classifier detects mislabeled instances by constructing a set of base-level detectors classifiers and then using their classification errors to identify misla beled instances  The general approach is to tag an instance as mislabeled if x of the m base level classifiers cannot classify it correctly  In this work we examine both majority and consensus filters A majority vote filter tags an instance as mislabeled if more than half of the m base level classifiers classify it incorrectly  A consensus filter requires that all base level detectors must fail to classify an instance as the class given by its training label for it to be eliminated from the training data It is important to note that the underlying premise of an ensemble filter differs from methods developed in regression analysis in which outliers are defined relative to a par ticular model Here we assume that some instances in the data have been mislabeled and that the label errors are independent of the particular model being fit to the data There fore collecting information from different models will provide a better method for detecting mislabeled instances than collecting information from a single model Scarcity of training data is a problem in many classification and learning problem do mains  e g   medical diagnosis   For such datasets  we want to minimize the probability