  Removing or filtering outliers and mislabeled instances prior to training a
learning algorithm has been shown to increase classification accuracy. A
popular approach for handling outliers and mislabeled instances is to remove
any instance that is misclassified by a learning algorithm. However, an
examination of which learning algorithms to use for filtering as well as their
effects on multiple learning algorithms over a large set of data sets has not
been done. Previous work has generally been limited due to the large
computational requirements to run such an experiment, and, thus, the
examination has generally been limited to learning algorithms that are
computationally inexpensive and using a small number of data sets. In this
paper, we examine 9 learning algorithms as filtering algorithms as well as
examining the effects of filtering in the 9 chosen learning algorithms on a set
of 54 data sets. In addition to using each learning algorithm individually as a
filter, we also use the set of learning algorithms as an ensemble filter and
use an adaptive algorithm that selects a subset of the learning algorithms for
filtering for a specific task and learning algorithm. We find that for most
cases, using an ensemble of learning algorithms for filtering produces the
greatest increase in classification accuracy. We also compare filtering with a
majority voting ensemble. The voting ensemble significantly outperforms
filtering unless there are high amounts of noise present in the data set.
Additionally, we find that a majority voting ensemble is robust to noise as
filtering with a voting ensemble does not increase the classification accuracy
of the voting ensemble.
