Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Ranking to Learn: Feature Ranking and Selection via Eigenvector
  Centrality   In an era where accumulating data is easy and storing it inexpensive, feature
selection plays a central role in helping to reduce the high-dimensionality of
huge amounts of otherwise meaningless data. In this paper, we propose a
graph-based method for feature selection that ranks features by identifying the
most important ones into arbitrary set of cues. Mapping the problem on an
affinity graph-where features are the nodes-the solution is given by assessing
the importance of nodes through some indicators of centrality, in particular,
the Eigen-vector Centrality (EC). The gist of EC is to estimate the importance
of a feature as a function of the importance of its neighbors. Ranking central
nodes individuates candidate features, which turn out to be effective from a
classification point of view, as proved by a thoroughly experimental section.
Our approach has been tested on 7 diverse datasets from recent literature
(e.g., biological data and object recognition, among others), and compared
against filter, embedded and wrappers methods. The results are remarkable in
terms of accuracy, stability and low execution time.

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The categories of this paper are: cond-mat
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The categories of this paper are: acc-phys
31
The categories of this paper are: ao-sci
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The categories of this paper are: dg-ga
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The categories of this paper are: hep-lat
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The categories of this paper are: patt-sol
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The categories of this paper are: hep-th
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The categories of this paper are: bayes-an
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The categories of this paper are: mtrl-th
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The categories of this paper are: alg-geom
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The categories of this paper are: q-bio
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The categories of this paper are: cmp-lg
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The categories of this paper are: q-fin
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The categories of this paper are: supr-con
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The categories of this paper are: quant-ph
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The categories of this paper are: atom-ph