Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Machine Learning-based Variability Handling in IoT Agents   Agent-based IoT applications have recently been proposed in several domains,
such as health care, smart cities and agriculture. Deploying these applications
in specific settings has been very challenging for many reasons including the
complex static and dynamic variability of the physical devices such as sensors
and actuators, the software application behavior and the environment in which
the application is embedded. In this paper, we propose a self-configurable IoT
agent approach based on feedback-evaluative machine-learning. The approach
involves: i) a variability model of IoT agents; ii) generation of sets of
customized agents; iii) feedback evaluative machine learning; iv) modeling and
composition of a group of IoT agents; and v) a feature-selection method based
on manual and automatic feedback.

37
The categories of this paper are: supr-con
32
The categories of this paper are: atom-ph
28
The categories of this paper are: alg-geom
26
The categories of this paper are: bayes-an
35
The categories of this paper are: mtrl-th
9
The categories of this paper are: nlin
13
The categories of this paper are: quant-ph
6
The categories of this paper are: astro-ph
7
The categories of this paper are: hep-th
33
The categories of this paper are: plasm-ph
16
The categories of this paper are: stat
4
The categories of this paper are: cond-mat
27
The categories of this paper are: comp-gas
17
The categories of this paper are: q-fin
20
The categories of this paper are: acc-phys
15
The categories of this paper are: nucl-ex