Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Novel Data Pre-processing Technique: Making Data Mining Robust to
  Different Units and Scales of Measurement   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.

6
The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph
12
The categories of this paper are: nucl-th
13
The categories of this paper are: quant-ph
32
The categories of this paper are: atom-ph
28
The categories of this paper are: alg-geom
20
The categories of this paper are: acc-phys
24
The categories of this paper are: dg-ga
35
The categories of this paper are: mtrl-th
34
The categories of this paper are: chem-ph
11
The categories of this paper are: hep-lat
36
The categories of this paper are: cmp-lg
4
The categories of this paper are: cond-mat
27
The categories of this paper are: comp-gas
30
The categories of this paper are: q-alg
3
The categories of this paper are: physics