Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Binary classification with ambiguous training data   In supervised learning, we often face with ambiguous (A) samples that are
difficult to label even by domain experts. In this paper, we consider a binary
classification problem in the presence of such A samples. This problem is
substantially different from semi-supervised learning since unlabeled samples
are not necessarily difficult samples. Also, it is different from 3-class
classification with the positive (P), negative (N), and A classes since we do
not want to classify test samples into the A class. Our proposed method extends
binary classification with reject option, which trains a classifier and a
rejector simultaneously using P and N samples based on the 0-1-$c$ loss with
rejection cost $c$. More specifically, we propose to train a classifier and a
rejector under the 0-1-$c$-$d$ loss using P, N, and A samples, where $d$ is the
misclassification penalty for ambiguous samples. In our practical
implementation, we use a convex upper bound of the 0-1-$c$-$d$ loss for
computational tractability. Numerical experiments demonstrate that our method
can successfully utilize the additional information brought by such A training
data.

36
The categories of this paper are: cmp-lg
19
The categories of this paper are: eess
11
The categories of this paper are: hep-lat
3
The categories of this paper are: physics
4
The categories of this paper are: cond-mat
32
The categories of this paper are: atom-ph
30
The categories of this paper are: q-alg
18
The categories of this paper are: econ
37
The categories of this paper are: supr-con
5
The categories of this paper are: gr-qc
9
The categories of this paper are: nlin
25
The categories of this paper are: patt-sol
0
The categories of this paper are: hep-ph
8
The categories of this paper are: hep-ex
24
The categories of this paper are: dg-ga
34
The categories of this paper are: chem-ph