Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Higher Order Mutual Information Approximation for Feature Selection   Feature selection is a process of choosing a subset of relevant features so
that the quality of prediction models can be improved. An extensive body of
work exists on information-theoretic feature selection, based on maximizing
Mutual Information (MI) between subsets of features and class labels. The prior
methods use a lower order approximation, by treating the joint entropy as a
summation of several single variable entropies. This leads to locally optimal
selections and misses multi-way feature combinations. We present a higher order
MI based approximation technique called Higher Order Feature Selection (HOFS).
Instead of producing a single list of features, our method produces a ranked
collection of feature subsets that maximizes MI, giving better comprehension
(feature ranking) as to which features work best together when selected, due to
their underlying interdependent structure. Our experiments demonstrate that the
proposed method performs better than existing feature selection approaches
while keeping similar running times and computational complexity.

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The categories of this paper are: hep-ex
10
The categories of this paper are: q-bio
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The categories of this paper are: hep-th
20
The categories of this paper are: acc-phys
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The categories of this paper are: nlin
30
The categories of this paper are: q-alg
3
The categories of this paper are: physics
28
The categories of this paper are: alg-geom
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The categories of this paper are: cmp-lg
26
The categories of this paper are: bayes-an
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The categories of this paper are: adap-org
29
The categories of this paper are: funct-an
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The categories of this paper are: hep-ph
15
The categories of this paper are: nucl-ex
4
The categories of this paper are: cond-mat
16
The categories of this paper are: stat