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[Apologies if you receive multiple copies of this CFP]<br>
<br>
<b>Extended deadline: 1 March 2014</b><br>
<div class="moz-text-html" lang="x-western"> <b><br>
Call for Papers: Neurocomputing Special Issue on "Advances in
Learning with Label Noise</b><b>"</b><br>
<br>
<b><u>AIMS AND SCOPE</u></b><br>
<br>
Label noise is an important issue in classification. It is both
expensive and difficult to obtain completely reliable labels, yet
traditional classifiers expect a perfectly labelled training set.
In real-world data sets, however, the labels available often
contain mistakes. Mislabelling may occur for several reasons,
including lack of information, speedy labelling by non-experts,
the subjective nature of class memberships, expert errors, and
communication problems. Furthermore, label noise may take several
different forms -- for instance, labelling errors may occur at
random, or may depend on particular values of the data features,
or they may be adversarial. Errors may affect all data classes
equally or asymmetrically. A large body of literature exists on
the effects of label noise, which shows that mislabelling may
detrimentally affect the classification performance, the
complexity of the learned models, and it may impair pre-processing
tasks such as feature selection. <br>
<br>
Many methods have been proposed to deal with label noise. Filter
approaches aim at identifying and removing any mislabelled
instances. Label noise sensitive algorithms aim at dealing with
label noise during learning, by modelling the process of label
corruption as part of modelling the data. Some methods have been
modified to take label noise into account in an embedded fashion.
The current literature on learning with label noise is a lively
mixture of theoretical and experimental studies which clearly
demonstrate both the complexity and the importance of the problem.
Dealing with mislabelled instances needs to be flexible enough to
accommodate label uncertainty, yet constrained enough to guide the
learning process in its decisions regarding when to trust the
label and when to trust the classifier.<br>
<br>
This special issue aims to stimulate new research in the area of
learning with label noise by providing a forum for authors to
report on new advances and findings in this problem area. Topics
of interest include, but are not limited to:<br>
<ul>
<li>new methods to deal with label noise;</li>
<li>new applications where label noise must be taken into
account;</li>
<li>theoretical results about learning in the presence of label
noise;</li>
<li>experimental results which provide insight about existing
methods;</li>
<li>dealing with different types of label noise (random,
non-random, malicious, or adversarial);</li>
<li>conditions for the consistency of classification in the
presence of label noise;</li>
<li> label noise in high dimensional small sample settings;</li>
<li> the issue of model meta-parameters/order selection in the
presence of label noise;</li>
<li>feature selection and dimensionality reduction in the
presence of label noise;</li>
<li>label-noise aware classification algorithms in static and
dynamic scenarios;</li>
<li>on-line learning with label noise</li>
<li>learning with side information to counter label noise;</li>
<li>model assessment in the presence of label noise in test
data.</li>
</ul>
<br>
<u><b>SUBMISSION OF MANUSCRIPTS</b></u><u><b><br>
</b></u><br>
If you intend to contribute to this special issue, please send a
title and abstract of your contribution to the guest editors.<br>
<br>
Authors should prepare their manuscript according to the Guide for
Authors available at <a
href="http://www.journals.elsevier.com/neurocomputing"
target="_blank">http://www.journals.elsevier.com/neurocomputing</a>.
All the papers will be peer-reviewed following the Neurocomputing
reviewing procedures. Authors must submit their papers
electronically by using online manuscript submission at <a
href="http://ees.elsevier.com/neucom" target="_blank">http://ees.elsevier.com/neucom</a>.
To ensure that all manuscripts are correctly included into the
special issue, it is important that authors select "SI: Learning
with label noise" when they reach the "Article Type" step in the
submission process.<br>
<br>
For technical questions regarding the submission website, please
contact the support office at Elsevier or the guest editors.<br>
<br>
<u><b>IMPORTANT DATES</b></u><br>
<br>
Extended deadline of paper submission: 1 March 2014<br>
Notification of acceptance: 15 July 2014<br>
<br>
<u><b>GUEST EDITORS</b></u><br>
<br>
Benoît Frénay (Managing Guest Editor)<br>
Université catholique de Louvain, Belgium<br>
E-mail: <a href="mailto:benoit.frenay@uclouvain.be"
target="_blank">benoit.frenay@uclouvain.be</a><br>
Website:<a href="http://bfrenay.wordpress.com/">
http://bfrenay.wordpress.com</a><br>
Phone: <a href="tel:%2B32%2010%20478133" value="+3210478133"
target="_blank">+32 10 478133</a><br>
<br>
Ata Kaban (Special Issue Guest Editor)<br>
University of Birmingham, United Kingdom<br>
E-mail: <a href="mailto:A.Kaban@cs.bham.ac.uk" target="_blank">A.Kaban@cs.bham.ac.uk</a><br>
Website:<a href="http://www.cs.bham.ac.uk/%7Eaxk">
http://www.cs.bham.ac.uk/~axk</a><br>
Phone: <a href="tel:%2B44%20121%2041%2042792"
value="+441214142792" target="_blank">+44 121 41 42792</a><br>
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