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\author{Habil Zare}

\begin{document}
\title{\textbf{FeaLect}: \textbf{Fea}ture se\textbf{Lect}ion \\by computing statistical scores}
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\tableofcontents


\begin{abstract} 
FeaLect is a feature selection method by statistically scoring the features.
Several random subsets are sampled from the input data and for each random subset, various linear models are fitted using lars method. 
For each feature, a score is calculated based on the tendency of LASSO in including that feature in the models.
\end{abstract}

\newpage
\section{Introduction} 
To build a robust classifier, the number of training instances is
usually required to be more than the number of features.
In many real life applications such as bioinformatics, natural language processing,
and computer vision, many features might be provided to the learning algorithm
without any prior knowledge on which ones should be used.
 Therefore,  the number of features can drastically
 exceed the number of training instances.
%We need to explain why this situation happens.
 Many regularization methods have been developed to prevent
 overfitting and improve the generalization error bound of
 the predictor in this learning situation.
%high variance of the predictor's performance
 Most notably, Lasso is an $\ell_1$-regularization technique
 for linear regression which has attracted much attention in
 machine learning and statistics. 
% Lasso is interesting for both machine learning and also statistics community.
Although efficient algorithms
 exist for recovering the whole regularization path \cite{efron2004} for
the  Lasso, finding a subset of highly relevant features
 which leads to a robust predictor is an important research question.
 %the best value for the regularization parameter



A well-known justification of $\ell_1$-regularization is that it
leads to {\it sparse} solutions, \emph{i.e.} those with many zeros, and
thus performs model selection. Recent research \cite{wainright2006,
zhaoandyu2006, bach2008, bach2009} have studied model {\it
consistency} of the Lasso (\emph{i.e.}, if we know the true sparsity pattern
of the underlying data-generation process, does the Lasso recover
this sparsity pattern when the number of training instances
increases?) Analysis in \cite{zhaoandyu2006, bach2008, bach2009} show
that for various decaying schemes of the regularization parameter,
Lasso selects the \emph{relevant} features with probability one and \emph{irrelevant}
features with positive probability as the number of training instances goes to infinity.
If several samples are available
from the underlying data distribution, irrelevant features can be
removed by simply interesting the set of selected features for each
sample. The idea in \cite{bach2008} is to provide such datasets by
re-sampling with replacement from the given training dataset using
the \emph{bootstrap} method \cite{efrontibshirani1998}. 



% In many published papers, the introduction ends up with a brief description of the method.
FeaLect \cite {zare2013scoring} proposes an alternative algorithm for feature selection based
on the Lasso for building a robust predictor. The hypothesis is that
defining a scoring scheme that measures the ``quality" of each
feature can provide a more robust selection of features.
FeaLect approach is to generate several samples from the training data,
determine the best relevance-ordering of the features for each
sample, and finally combine these relevance-orderings to select
highly relevant features. 

\section{How to use FeaLect?}
FeaLect is an R package source that can be downloaded from The Comprehensive R Archive Network (CRAN). 
In Linux, it can be installed by the following command:
\newline
$\\$
\texttt{R CMD INSTALL FeaLect\_x.y.z.tar.gz}
\newline
$\\$
where x.y.z. determines the version.

The main function of this package is FeaLect() which is loaded by using the command library(FeaLect) in R. 

\subsection{An example}

This example shows how FeaLect can be run to assign scores to features. Here, $F$ is a feature matrix; each column is a feature and 
each row represents a sample. $L$ is the label vector that contains 1 and 0 for positive and negative samples. We assume $L$ is 
ordered according to the rows of $F$. 

<<FeaLect_example,fig=FALSE, echo=TRUE>>=
	library(FeaLect)
	data(mcl_sll)
	F <- as.matrix(mcl_sll[ ,-1])	# The Feature matrix
	L <- as.numeric(mcl_sll[ ,1])	# The labels
	names(L) <- rownames(F)
	message(dim(F)[1], " samples and ",dim(F)[2], " features.")

	FeaLect.result <-FeaLect(F=F,L=L,maximum.features.num=10, 
                                 total.num.of.models=100, talk=TRUE)
@

The scores are returned in $log.score$ element of the output:

<<FeaLect_example,fig=TRUE, echo=TRUE>>=
	plot(FeaLect.result$log.scores, pch=19)
@



Besides the scores, FeaLect() function computes some other values as well.
For instance, the features selected by Bolasso method are also returned as a biproduct without increasing computational cost. 
Moreover, the package includes some other functions.  The input structures and output values are detailed in the package manual. 

\bibliographystyle{plain}
\bibliography{overfitting} %your .bib file



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