posted by user: bfrenay || 4308 views || tracked by 12 users: [display]

NEUCOM 2014 : Neurocomputing Special Issue: Advances in Learning with Label Noise


When N/A
Where N/A
Submission Deadline Mar 1, 2014
Notification Due Jul 15, 2014
Categories    machine learning

Call For Papers

Call for Papers: Neurocomputing Special Issue on "Advances in Learning with Label Noise"


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.

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.

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:

- new methods to deal with label noise;
- new applications where label noise must be taken into account;
- theoretical results about learning in the presence of label noise;
- experimental results which provide insight about existing methods;
- dealing with different types of label noise (random, non-random, malicious, or adversarial);
- conditions for the consistency of classification in the presence of label noise;
- label noise in high dimensional small sample settings;
- the issue of model meta-parameters/order selection in the presence of label noise;
- feature selection and dimensionality reduction in the presence of label noise;
- label-noise aware classification algorithms in static and dynamic scenarios;
- on-line learning with label noise
- learning with side information to counter label noise;
- model assessment in the presence of label noise in test data.


If you intend to contribute to this special issue, please send a title and abstract of your contribution to the guest editors.

Authors should prepare their manuscript according to the Guide for Authors available at All the papers will be peer-reviewed following the Neurocomputing reviewing procedures. Authors must submit their papers electronically by using online manuscript submission at 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.

For technical questions regarding the submission website, please contact the support office at Elsevier or the guest editors.


Deadline of paper submission: 15 February 2014
Notification of acceptance: 15 July 2014


Benoît Frénay (Managing Guest Editor)
Université catholique de Louvain, Belgium
Phone: +32 10 478133

Ata Kaban (Special Issue Guest Editor)
University of Birmingham, United Kingdom
Phone: +44 121 41 42792

Related Resources

CMES_RADLMSA 2020   CMES_Recent Advances on Deep Learning for Medical Signal Analysis (IF: 0.796)
IWUAS 2020   2020 International Workshop on Unmanned Aircraft Systems (IWUAS 2020)
ASPAI 2020   2nd International Conference on Advances in Signal Processing and Artificial Intelligence
ISBDAI 2020   【Ei Compendex Scopus】2018 International Symposium on Big Data and Artificial Intelligence
Journal Special Issue 2019   Machine Learning on Scientific Data and Information
SDM 2020   SIAM International Conference on Data Mining
ICMV--SPIE, Scopus, Ei Compendex 2019   SPIE--2019 The 12th International Conference on Machine Vision (ICMV 2019)--EI, Scopus and ISI
Data Science 2020   3nd Annual International Great Lakes Data Science Symposium
IEEE COINS 2020   Internet of Things IoT | Artificial Intelligence | Machine Learning | Big Data | Blockchain | Edge & Cloud Computing | Security | Embedded Systems | Circuit and Systems | WSN | 5G
ICAIML 2019   【EI SCOPUS CPCI】2019 1st International Conference on Artificial Intelligence and Machine Learning