Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Classification from Positive and Biased Negative Data with Skewed
  Labeled Posterior Probability   The binary classification problem has a situation where only biased data are
observed in one of the classes. In this paper, we propose a new method to
approach the positive and biased negative (PbN) classification problem, which
is a weakly supervised learning method to learn a binary classifier from
positive data and negative data with biased observations. We incorporate a
method to correct the negative impact due to skewed confidence, which
represents the posterior probability that the observed data are positive. This
reduces the distortion of the posterior probability that the data are labeled,
which is necessary for the empirical risk minimization of the PbN
classification problem. We verified the effectiveness of the proposed method by
numerical experiments and real data analysis.

29
The categories of this paper are: funct-an
30
The categories of this paper are: q-alg
10
The categories of this paper are: q-bio
34
The categories of this paper are: chem-ph
31
The categories of this paper are: ao-sci
27
The categories of this paper are: comp-gas
9
The categories of this paper are: nlin
17
The categories of this paper are: q-fin
18
The categories of this paper are: econ
6
The categories of this paper are: astro-ph
24
The categories of this paper are: dg-ga
7
The categories of this paper are: hep-th
15
The categories of this paper are: nucl-ex
13
The categories of this paper are: quant-ph
23
The categories of this paper are: solv-int
8
The categories of this paper are: hep-ex