Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Data-driven chimney fire risk prediction using machine learning and
  point process tools   Chimney fires constitute one of the most commonly occurring fire types.
Precise prediction and prompt prevention are crucial in reducing the harm they
cause. In this paper, we develop a combined machine learning and statistical
modeling process to predict chimney fires. Firstly, we use random forests and
permutation importance techniques to identify the most informative explanatory
variables. Secondly, we design a Poisson point process model and apply
associated logistic regression estimation to estimate the parameters. Moreover,
we validate the Poisson model assumption using second-order summary statistics
and residuals. We implement the modeling process on data collected by the
Twente Fire Brigade and obtain plausible predictions. Compared to similar
studies, our approach has two advantages: i) with random forests, we can select
explanatory variables non-parametrically considering variable dependence; ii)
using logistic regression estimation, we can fit the statistical model
efficiently by tuning it to focus on important regions and times of the fire
data.

21
The categories of this paper are: adap-org
27
The categories of this paper are: comp-gas
9
The categories of this paper are: nlin
33
The categories of this paper are: plasm-ph
34
The categories of this paper are: chem-ph
28
The categories of this paper are: alg-geom
2
The categories of this paper are: math
24
The categories of this paper are: dg-ga
17
The categories of this paper are: q-fin
29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
19
The categories of this paper are: eess
20
The categories of this paper are: acc-phys
11
The categories of this paper are: hep-lat
13
The categories of this paper are: quant-ph
14
The categories of this paper are: math-ph