Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Conformal Rule-Based Multi-label Classification   We advocate the use of conformal prediction (CP) to enhance rule-based
multi-label classification (MLC). In particular, we highlight the mutual
benefit of CP and rule learning: Rules have the ability to provide natural
(non-)conformity scores, which are required by CP, while CP suggests a way to
calibrate the assessment of candidate rules, thereby supporting better
predictions and more elaborate decision making. We illustrate the potential
usefulness of calibrated conformity scores in a case study on lazy multi-label
rule learning.

31
The categories of this paper are: ao-sci
2
The categories of this paper are: math
30
The categories of this paper are: q-alg
22
The categories of this paper are: chao-dyn
24
The categories of this paper are: dg-ga
20
The categories of this paper are: acc-phys
6
The categories of this paper are: astro-ph
5
The categories of this paper are: gr-qc
13
The categories of this paper are: quant-ph
32
The categories of this paper are: atom-ph
25
The categories of this paper are: patt-sol
15
The categories of this paper are: nucl-ex
36
The categories of this paper are: cmp-lg
26
The categories of this paper are: bayes-an
10
The categories of this paper are: q-bio
21
The categories of this paper are: adap-org