paper.tex

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\Title{{A benchmark of data stream classification
for human activity recognition
on connected objects}}

\firstpage{1} 
\makeatletter 
\setcounter{page}{\@firstpage} 
\makeatother
\pubvolume{xx}
\issuenum{1}
\articlenumber{5}
\pubyear{2020}
\copyrightyear{2020}
%\externaleditor{Academic Editor: name}
\history{Received: date; Accepted: date; Published: date}

% Author Orchid ID: enter ID or remove command
\newcommand{\orcidauthorA}{0000-0003-2129-5517} % Add \orcidA{} behind the author's name
\newcommand{\orcidauthorB}{0000-0003-2620-5883} % Add \orcidB{} behind the author's name

% Authors, for the paper (add full first names)
\Author{Martin Khannouz$^1$\orcidA{}, Tristan Glatard$^1$\orcidB{}}


% Authors, for metadata in PDF
\AuthorNames{Martin Khannouz and Tristan Glatard}

% Affiliations / Addresses (Add [1] after \address if there is only one affiliation.)
\address{$^{1}$ \quad Department of Computer Science and Software Engineering, Concordia University}

% Contact information of the corresponding author
\corres{Correspondence: martin.khannouz@gmail.com, tristan.glatard@concordia.ca}

% Current address and/or shared authorship
%\firstnote{Current address: Affiliation 3} 
%\secondnote{These authors contributed equally to this work.}
% The commands \thirdnote{} till \eighthnote{} are available for further notes

%\simplesumm{} % Simple summary

\abstract{
This paper evaluates data stream classifiers from
the perspective of connected devices, focusing on
the use case of Human Activity Recognition. We
measure both classification performance and
resource consumption (runtime, memory, and power)
of five usual stream classification algorithms,
implemented in a consistent library, and applied
to two real human activity datasets and to three
synthetic datasets.  Regarding classification
performance, results show an overall superiority
of the Hoeffding Tree, the Mondrian forest, and
the Naïve Bayes classifiers over the Feedforward
Neural Network and the Micro Cluster Nearest
Neighbor classifiers on 4 datasets out of 6,
including the real ones. In addition, the
Hoeffding Tree, and to some extent the Micro
Cluster Nearest Neighbor, are the only classifiers
that can recover from a concept drift. Overall,
the three leading classifiers still perform
substantially lower than an offline classifier on
the real datasets. Regarding resource consumption,
the Hoeffding Tree and the Mondrian forest are the
most memory intensive and have the longest
runtime, however, no difference in power
consumption is found between classifiers. We
conclude that stream learning for Human Activity
Recognition on connected objects is challenged by
two factors which could lead to interesting future
work: a high memory consumption and low F1 scores
overall.
}

% Keywords
\keyword{
Application Platform; Data Management and
Analytics; Smart Environment; Data
Streams; Classification; Power; Memory Footprint;
Benchmark; Human Activity
Recognition; MCNN; Mondrian; Hoeffding Tree;}
\begin{document}
\maketitle
\input{abstract.tex}
\input{introduction.tex}
\input{method.tex}
\input{results.tex}
\input{conclusion.tex}

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