  Not all instances in a data set are equally beneficial for inducing a model
of the data. Some instances (such as outliers or noise) can be detrimental.
However, at least initially, the instances in a data set are generally
considered equally in machine learning algorithms. Many current approaches for
handling noisy and detrimental instances make a binary decision about whether
an instance is detrimental or not. In this paper, we 1) extend this paradigm by
weighting the instances on a continuous scale and 2) present a methodology for
measuring how detrimental an instance may be for inducing a model of the data.
We call our method of identifying and weighting detrimental instances reduced
detrimental instance learning (RDIL). We examine RIDL on a set of 54 data sets
and 5 learning algorithms and compare RIDL with other weighting and filtering
approaches. RDIL is especially useful for learning algorithms where every
instance can affect the classification boundary and the training instances are
considered individually, such as multilayer perceptrons trained with
backpropagation (MLPs). Our results also suggest that a more accurate estimate
of which instances are detrimental can have a significant positive impact for
handling them.
