Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Exploring Correlation between Labels to improve Multi-Label
  Classification   This paper attempts multi-label classification by extending the idea of
independent binary classification models for each output label, and exploring
how the inherent correlation between output labels can be used to improve
predictions. Logistic Regression, Naive Bayes, Random Forest, and SVM models
were constructed, with SVM giving the best results: an improvement of 12.9\%
over binary models was achieved for hold out cross validation by augmenting
with pairwise correlation probabilities of the labels.

18
The categories of this paper are: econ
32
The categories of this paper are: atom-ph
35
The categories of this paper are: mtrl-th
33
The categories of this paper are: plasm-ph
15
The categories of this paper are: nucl-ex
10
The categories of this paper are: q-bio
30
The categories of this paper are: q-alg
28
The categories of this paper are: alg-geom
25
The categories of this paper are: patt-sol
37
The categories of this paper are: supr-con
12
The categories of this paper are: nucl-th
16
The categories of this paper are: stat
19
The categories of this paper are: eess
9
The categories of this paper are: nlin
24
The categories of this paper are: dg-ga
8
The categories of this paper are: hep-ex