  It is widely known in the machine learning community that class noise can be
(and often is) detrimental to inducing a model of the data. Many current
approaches use a single, often biased, measurement to determine if an instance
is noisy. A biased measure may work well on certain data sets, but it can also
be less effective on a broader set of data sets. In this paper, we present
noise identification using classifier diversity (NICD) -- a method for deriving
a less biased noise measurement and integrating it into the learning process.
To lessen the bias of the noise measure, NICD selects a diverse set of
classifiers (based on their predictions of novel instances) to determine which
instances are noisy. We examine NICD as a technique for filtering, instance
weighting, and selecting the base classifiers of a voting ensemble. We compare
NICD with several other noise handling techniques that do not consider
classifier diversity on a set of 54 data sets and 5 learning algorithms. NICD
significantly increases the classification accuracy over the other considered
approaches and is effective across a broad set of data sets and learning
algorithms.
