Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Dynamic Decision Boundary for One-class Classifiers applied to
  non-uniformly Sampled Data   A typical issue in Pattern Recognition is the non-uniformly sampled data,
which modifies the general performance and capability of machine learning
algorithms to make accurate predictions. Generally, the data is considered
non-uniformly sampled when in a specific area of data space, they are not
enough, leading us to misclassification problems. This issue cut down the goal
of the one-class classifiers decreasing their performance. In this paper, we
propose a one-class classifier based on the minimum spanning tree with a
dynamic decision boundary (OCdmst) to make good prediction also in the case we
have non-uniformly sampled data. To prove the effectiveness and robustness of
our approach we compare with the most recent one-class classifier reaching the
state-of-the-art in most of them.

19
The categories of this paper are: eess
10
The categories of this paper are: q-bio
34
The categories of this paper are: chem-ph
32
The categories of this paper are: atom-ph
35
The categories of this paper are: mtrl-th
3
The categories of this paper are: physics
33
The categories of this paper are: plasm-ph
4
The categories of this paper are: cond-mat
9
The categories of this paper are: nlin
15
The categories of this paper are: nucl-ex
26
The categories of this paper are: bayes-an
2
The categories of this paper are: math
12
The categories of this paper are: nucl-th
24
The categories of this paper are: dg-ga
21
The categories of this paper are: adap-org
28
The categories of this paper are: alg-geom