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COST 2018 : International Workshop on Cost-Sensitive Learning (at SDM'2018)


When May 3, 2018 - May 5, 2018
Where San Diego, California
Submission Deadline Feb 11, 2018
Notification Due Feb 25, 2018
Final Version Due Mar 11, 2018
Categories    computer science   machine learning   data mining   data science

Call For Papers

International Workshop on Cost-Sensitive Learning (COST 2018, co-located with SDM 2018)
3-5th May, 2018
San Diego, California, USA


The proceedings of this workshop will be published as a volume of the Proceedings of Machine Learning Research (PMLR) series.



Submission Deadline (Extended): Saturday, February 11, 2018
Notification of Acceptance: Sunday, February 25, 2018
Camera-ready Deadline: Sunday, March 11, 2018
SDM 2018: 3-5th May, 2018
COST'2018: TBA


Research on data mining and machine learning tasks are commonly developed under assumptions of uniform preferences, where cases are equally important, and issues such as data acquisition costs are not considered. However, many real-world data-mining applications involve complex settings where such assumptions do not apply. Frequently, predictive analytics involve settings where the consideration of costs is unavoidable. Such costs can appear at all stages of the data mining process, e.g. data acquisition, modelling or model application. In this workshop we will target tasks involving the consideration of costs and/or benefits which may arise from different sources.

The most studied setting regards binary classification tasks with costs applied at the evaluation level. In this case, different penalizations and/or benefits are assigned to different mistakes and/or accurate predictions, and a cost matrix is used to assess the performance of model. However, other settings may also be cost dependent such as regression and time series or data streams forecasting tasks. Moreover, there are other issues which, although relevant, are still unsolved or need improved solutions, such as performance evaluation and applications involving unsupervised and semi-supervised tasks.

Tackling the issues raised by cost-sensitive learning problems is crucial to both academia and industry, as it allows the development of more suitable and robust systems for complex settings. For industry partners, this presents the opportunity to develop frameworks targeting specific contexts, embedding in the solutions the necessary domain knowledge. Examples include dealing with budgeted resources, limited space or computational time, prediction of rare events and anomaly detection.

This workshop will bring together practitioners and researchers from both academia and industry that are linked to all levels of cost-sensitive learning. This will promote a wider knowledge exchange as well as the interaction between different agents. Our workshop invites inter-disciplinary contributions to tackle the problems that many real-world domains face nowadays, in order to promote significant developments in this field.

The research topics of interest to COST'2018 workshop include (but are not limited to) the following:

*** Foundations of cost- and utility-based learning
Probabilistic and statistical models
New knowledge discovery theories and models
Deep learning in the context of cost-sensitive learning
Handling cost-sensitive big data
Learning with non i.i.d. data
Relations between cost/utility-based learning and data pre-processing/post-processing
Sampling approaches
Feature selection and feature transformation
Evaluation in cost-sensitive learning

*** Knowledge discovery and machine learning in cost and utility-based tasks
Classification, ordinal classification
Data streams and time series forecasting
Outlier detection
Adaptive learning and algorithm-level approaches
Multi-label, multi-instance, sequence and association rules mining
Active learning
Spatial and spatio-temporal learning

*** Applications of cost and utility-based learning
Budgeted applications
Fraud detection (e.g. finance, credit and online banking)
Anomaly detection (e.g. industry, intrusion detection)
Health applications
Environmental applications (e.g. meteorology, biology)
Social media applications (e.g. popularity prediction, recommender systems)
Real world applications (e.g. oil spill detection)
Case studies



This workshop accepts two types of submissions: Full and Short (Poster) Papers
For each of the accepted full papers, a presentation slot of 20 minutes is provided.
As for short papers, these will be introduced with short presentations, and a poster session will be organized.

* The maximum length for full papers is 12 pages and for the short papers the limit is 10 pages. Papers not respecting such limit will be rejected.
* All submissions must be written in English and follow the PMLR format. Instructions for authors and style files may be found in
* All submissions will be reviewed by the Program Committee using a double-blind method. As such, it is required that no personal information or reference to the authors should be introduced in the submitted paper.
* Full papers that have already been accepted or are currently under review for other workshops, conferences, or journals will not be considered.
* Submissions will be evaluated concerning their technical quality, relevance, significance, originality and clarity.
* At least one author of each accepted paper must attend the workshop and present the paper.

To submit a paper, authors must use the on-line submission system hosted in EasyChair:



All accepted papers will be included in the workshop proceedings, published as a volume in Proceedings of Machine Learning Research (PMLR).

Additionally, based on the success of the workshop, authors of selected papers may be invited to submit extended versions of their manuscripts to a premier journal concerning the topics of this workshop.



Naoki Abe, IBM
Roberto Alejo, Tecnológico de Estudios Superiores de Jocotitlán
Colin Bellinger, University of Alberta
Seppe Vanden Broucke, Katholieke Universiteit Leuven
Nitesh Chawla, University of Notre Dame
Christopher Drummond, National Research Council Canada
Ines Dutra, DCC - FCUP
Tom Fawcett, Apple
Mikel Galar, Universidad Pública de Navarra
Nathalie Japkowicz, American University
Charles Ling, Western University
Dragos Margineantu, Boeing Research & Technology
Ronaldo Prati, Universidade Federal do ABC
Foster Provost, NYU Stern
Jose Hernandez-Orallo, Universitat Politècnica de València
Rita Ribeiro, LIAAD / INESC Tec
Shengli Victor Sheng, University of Central Arkansas
Marina Sokolova, University of Ottawa



Luis Torgo | Department of Computer Science - University of Porto, LIAAD - INESC TEC
Stan Matwin | Faculty of Computer Science, Dalhousie University
Gary Weiss | Department of Computer and Information Science, Fordham University
Nuno Moniz | Department of Computer Science - University of Porto, LIAAD - INESC TEC
Paula Branco | Department of Computer Science - University of Porto, LIAAD - INESC TEC

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