  Many existing data mining algorithms use feature values directly in their
model, making them sensitive to units/scales used to measure/represent data.
Pre-processing of data based on rank transformation has been suggested as a
potential solution to overcome this issue. However, the resulting data after
pre-processing with rank transformation is uniformly distributed, which may not
be very useful in many data mining applications. In this paper, we present a
better and effective alternative based on ranks over multiple sub-samples of
data. We call the proposed pre-processing technique as ARES | Average Rank over
an Ensemble of Sub-samples. Our empirical results of widely used data mining
algorithms for classification and anomaly detection in a wide range of data
sets suggest that ARES results in more consistent task specific? outcome across
various algorithms and data sets. In addition to this, it results in better or
competitive outcome most of the time compared to the most widely used min-max
normalisation and the traditional rank transformation.
