Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improved mutual information measure for classification and community
  detection   The information theoretic quantity known as mutual information finds wide use
in classification and community detection analyses to compare two
classifications of the same set of objects into groups. In the context of
classification algorithms, for instance, it is often used to compare discovered
classes to known ground truth and hence to quantify algorithm performance. Here
we argue that the standard mutual information, as commonly defined, omits a
crucial term which can become large under real-world conditions, producing
results that can be substantially in error. We demonstrate how to correct this
error and define a mutual information that works in all cases. We discuss
practical implementation of the new measure and give some example applications.

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The categories of this paper are: hep-ph
11
The categories of this paper are: hep-lat
22
The categories of this paper are: chao-dyn
20
The categories of this paper are: acc-phys
37
The categories of this paper are: supr-con
8
The categories of this paper are: hep-ex
35
The categories of this paper are: mtrl-th
36
The categories of this paper are: cmp-lg
6
The categories of this paper are: astro-ph
13
The categories of this paper are: quant-ph
30
The categories of this paper are: q-alg
26
The categories of this paper are: bayes-an
5
The categories of this paper are: gr-qc
21
The categories of this paper are: adap-org
9
The categories of this paper are: nlin
24
The categories of this paper are: dg-ga