Print

WCCI2018 - Special session on "Ordinal and monotonic classification"

This session has been provisionally accepted for the IJCNN 2018 conference which will be held as part of the 2018 IEEE WCCI in Rio de Janeiro, Brazil at July 8-13, 2018.

Scope

Ordinal classification covers those classification tasks where the different labels show an ordering relation, which is related to the nature of the target variable. For example, financial trading could be assisted by ordinal classification techniques predicting not only a binary decision of buying an asset, but also the amount of investment. The decision could be categorized by {“no investment”, “low investment”, “medium investment”, “huge investment”}. Machine learning methods should consider the natural order among the classes and penalize differently the errors. Specific solutions have been recently proposed in the machine learning and pattern recognition literature, resulting in a very active field. Moreover, if a set of monotonicity constraints between independent and dependent variables has to be satisfied, then the problem is known as monotonic classification. In these problems, a higher value of an attribute in an example, fixing other values, should not decrease its class assignment. The monotonicity of relations between the dependent and explanatory variables is very usual as a prior knowledge form in data classification and it should be exploited to obtain more robust models.

This special session aims to cover a wide range of works and recent advances on ordinal and monotonic classification. We hope that this session can provide a common forum for researchers and practitioners to exchange their ideas and report their latest finding in the area.

Topics

In particular we encourage submissions addressing the following issues:

  • Extensions of standard classification methods to ordinal and monotonic classification (support vector machines, Gaussian processes, discriminant analysis, etc).
  • Extensions of deep learning techniques to ordinal and monotonic classification.
  • Threshold models and decomposition methods for ordinal classification.
  • Non-standard predictive problems with ordering relation or monotonic constraints: Imbalanced classification, semi-supervised, multi-label or multi-instance learning.
  • Clustering and pre-processing methods for ordinal and monotonic data (data cleaning techniques, feature selection, over-sampling, under-sampling etc).
  • Evaluation measures for ordinal and monotonic classification.
  • Preference learning.
  • Data preprocessing (feature selection, noise filtering, etc...) for ordinal and monotonic classification.
  • Applications in medicine, information retrieval, recommendation systems, risk analysis… and any other real-world problems.

Paper submission

If you are interested in taking part on this special session, please submit your paper directly through the IJCNN 2018 website selecting the following option for the "Main research topic": "Ordinal and monotonic classification". You can find further information related to the submission process and important dates at the conference website.

Papers submitted for special sessions are to be peer-reviewed with the same criteria used for the rest of contributed papers. As a result, all accepted papers will be included in the proceedings of IJCNN2018.

Important dates

  • 15 December 2017 – Tutorial, Special Sessions, Workshop and Competition Proposals
  • 1st February 2018 – Paper Submission
  • 15th March 2018 – Paper Acceptance
  • 1st May 2018 – Final Paper Submission
  • 1st May 2018 – Early Registration
  • 8-13 July 2018 – IEEE WCCI 2018, Rio de Janeiro, Brazil

Organizers

  • Pedro A. Gutiérrez, Dept. of Computer Science and Numerical Analysis, University of Cordoba, Spain. Email address: This email address is being protected from spambots. You need JavaScript enabled to view it.
  • Salvador García, Department of Computer Science and Artificial Intelligence, University of Granada, Spain. This email address is being protected from spambots. You need JavaScript enabled to view it.