ICDE.tex

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% paper title
% can use linebreaks \\ within to get better formatting as desired
\title{Scalable Learning of Tree-Based Models on Sparsely Representable Data}

% author names and affiliations
% use a multiple column layout for up to three different
% affiliations
\author{
\IEEEauthorblockN{Fares Hedayati}
\IEEEauthorblockA{Elance-oDesk\\
                  Dept. of Data Science\\
                  441 Logue Ave, Mountain View, CA 94043\\
                  Email: fares19@elance-odesk.com}
\and
\IEEEauthorblockN{Arnaud Joly}
\IEEEauthorblockA{Dept. of EE \& CS \& GIGA-R\\
                  University of Liège\\
                  Belgium\\
                  Email: a.joly@ulg.ac.be
}
\and
\IEEEauthorblockN{Panagiotis Papadimitriou}
\IEEEauthorblockA{Elance-oDesk\\
                  Dept. of Data Science\\
                  441 Logue Ave, Mountain View, CA 94043\\
                  Email: papadimitriou@elance-odesk.com}
}

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%\author{\IEEEauthorblockN{Michael Shell\IEEEauthorrefmark{1},
%Homer Simpson\IEEEauthorrefmark{2},
%James Kirk\IEEEauthorrefmark{3},
%Montgomery Scott\IEEEauthorrefmark{3} and
%Eldon Tyrell\IEEEauthorrefmark{4}}
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%Georgia Institute of Technology,
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%\IEEEauthorblockA{\IEEEauthorrefmark{2}Twentieth Century Fox, Springfield, USA\\
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%Telephone: (800) 555--1212, Fax: (888) 555--1212}
%\IEEEauthorblockA{\IEEEauthorrefmark{4}Tyrell Inc., 123 Replicant Street, Los Angeles, California 90210--4321}}

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\begin{document}
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\begin{abstract}

Many machine learning tasks such as text annotation usually require
training over very big datasets, e.g., millions of web documents,
that can be represented in a sparse input space. State-of-the-art tree-based ensemble
algorithms cannot scale to such datasets, since they include operations whose running time 
is a function of the input space size rather than a function of the non-zero input elements. %and
%implementations have focused on tasks with dense input space, while at most
%emulating random memory access on sparse input data. 
In this paper, we propose an efficient splitting algorithm to leverage
input sparsity within decision tree
methods. %We exploit the a-priori knowledge that each column has very few non-
%zero elements and show how learning time is significantly decreased.
Our algorithm improves training time over sparse datasets by more than
two orders of magnitude and it will be incorporated in the next version
of \emph{scikit-learn}, the most popular open source Python machine learning library.

%Many machine learning tasks such as text annotation involve sparsely
%representable input space. State-of-the-art tree-based ensemble algorithms and
%implementations have focused on tasks with dense input space, while at most
%emulating random memory access on sparse input data. We propose a fast and an
%efficient splitting algorithm to leverage input sparsity within decision tree
%methods. We exploit the a-priori knowledge that each column have very few non-
%zero elements and show how learning time is significantly decreased.

\end{abstract}
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% no keywords

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\input{intro.tex}
\input{dense.tex}
\input{sparse.tex}
\input{experiment.tex}

\section{Conclusion}\label{sec:conclusion}
We proposed a method for building tree-based models with sparse input
support.  Our method takes advantage of input sparsity by avoiding
sorting sample sets of a node along a feature unless they are nonzero
at that feature. This approach speeds up training substantially as
sorting is a costly but essential and ubiquitous component of
tree-based models.

\section{Acknowledgment} % use section* for acknowledgement
Arnaud Joly is a research fellow of the FNRS, Belgium. This work is
partially supported by PASCAL2 and the IUAP DYSCO, initiated by the
Belgian State, Science Policy Office.

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\end{document}