Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: MOB-ESP and other Improvements in Probability Estimation   A key prerequisite to optimal reasoning under uncertainty in intelligent
systems is to start with good class probability estimates. This paper improves
on the current best probability estimation trees (Bagged-PETs) and also
presents a new ensemble-based algorithm (MOB-ESP). Comparisons are made using
several benchmark datasets and multiple metrics. These experiments show that
MOB-ESP outputs significantly more accurate class probabilities than either the
baseline BPETs algorithm or the enhanced version presented here (EB-PETs).
These results are based on metrics closely associated with the average accuracy
of the predictions. MOB-ESP also provides much better probability rankings than
B-PETs. The paper further suggests how these estimation techniques can be
applied in concert with a broader category of classifiers.

34
The categories of this paper are: chem-ph
27
The categories of this paper are: comp-gas
12
The categories of this paper are: nucl-th
11
The categories of this paper are: hep-lat
14
The categories of this paper are: math-ph
0
The categories of this paper are: hep-ph
37
The categories of this paper are: supr-con
24
The categories of this paper are: dg-ga
31
The categories of this paper are: ao-sci
15
The categories of this paper are: nucl-ex
3
The categories of this paper are: physics
4
The categories of this paper are: cond-mat
26
The categories of this paper are: bayes-an
7
The categories of this paper are: hep-th
19
The categories of this paper are: eess
6
The categories of this paper are: astro-ph