COST'2018: Cost-Sensitive Learning Workshop (with SIAM SDM 2018) [PMLR Proceedings] - 3 weeks to deadline

[Nuno Moniz]

*Apologies for cross-posting*

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

Website: http://cost.dcc.fc.up.pt/
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The proceedings of this workshop will be published as a volume of the  
Proceedings of Machine Learning Research (PMLR) series.

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KEY DATES

Submission Deadline: Friday, January 26, 2018
Notification of Acceptance: Sunday, February 25, 2018
Camera-ready Deadline: Sunday, March 11, 2018
SDM 2018: 3-5th May, 2018
COST'2018: TBA

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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
Regression
Data streams and time series forecasting
Clustering
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

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SUBMISSION

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  
http://cost.dcc.fc.up.pt/ManuscriptPMLR.zip
* 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: https://easychair.org/conferences/?conf=cost2018

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PROCEEDINGS

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.

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PROGRAM COMMITTEE

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, Silicon Valley Data Science
Mikel Galar, Universidad Pública de Navarra
Nathalie Japkowicz, American University
Charles Ling, Western University
Dragos Margineantu, Boeing Research and 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

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ORGANIZERS

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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