Learning from imbalanced data
You try to build a model, but it is biased towards the class that is better represented in the dataset.
Classifier Learning from Difficult Data
The workshop on Classifier Learning from Difficult Data is organized during the 27TH EUROPEAN CONFERENCE ON ARTIFICIAL INTELLIGENCE in Santiago de Compostella.
The pre-conference program, including the CLD2 workshop, will take place in two adjacent buildings on the North Campus of the University of Santiago de Compostela on October 19-20, 2024.
CLD2, as a half-day event, will consist of two 90-minute sessions, separated by a 30-minute coffee break. Exact information as to the day, place, and time of the workshop will be provided later by the organizers.
About
Nowadays, many practical decision tasks require to build models based on data which included serious difficulties, as imbalanced class distributions, a high number of classes, high-dimensional features, a small or extremely high number of learning examples, limited access to ground truth, data incompleteness, or data in motion, to enumerate only a few. Such characteristics may strongly deteriorate the final model performances. Therefore, the proposition of the new learning methods that can combat the aforementioned difficulties should focus on intense research. The main aim of this workshop is to discuss the problems of data difficulties, identify new issues, and shape future directions for research.
Topics of interest
Learning from imbalanced data streams, including concept drift management
The situation turns out to be even more difficult than in the previous case, because the data arrives (potentially) forever.
Learning from multi-view/multimodal data
You solve the problem of the curse of dimensionality through space decomposition and ensemble methods.
Automated machine learning
As in meta-learning, you try to give the method full control over the learning process.
Life-long machine learning
You already have a working model, but it turns out that it should solve a new task. And you really don't want to train it from the ground up.
Learning with limited ground truth access
You have experts to label the data, but there are a million objects and only three experts.
Learning in a open set
You're training your model to tell dogs from cats, but you also want to know what happens when you show it a raccoon.
Learning from high dimensional data
In the general case, you have a very large number of features in the set, but you don't want to solve this problem with multi-view approaches.
Learning with a high number of classes
Sometimes there are more classes than objects in a set.
Learning from massive data, including instance and prototype selection
You are trying to manage the problem of a very large dataset by initially sorting it out and finding the most valuable instances.
Learning based on limited data sets, including one-shot learning
It turns out that your data set is not massive. On the contrary, it covers only a few cases. What are you doing?
Learning from incomplete data
Or maybe the data set is not too small, but it turns out to be extremely leaky?
Case studies and real-world applications
Share your struggles with the real datasets!
Key dates
In addition to regular paper submissions, the CLD2 Workshop may accept papers rejected from the main conference purely based on the previously written reviews (made available by the PC chairs). We invite potential authors to submit a request for their rejected paper to be considered by 11 July 2024. The decision on these papers will be made by 18 July 2024. Articles rejected from the main conference should be submitted using the submission system, choosing the appropriate submission type. Once submissions are received, CLD2 workshop organizers will ask ECAI24 PC Chairs for the main conference reviews.
Paper submission deadline
Author notification date
Requests for consideration of papers rejected from the main conference
Notification date for papers rejected from the main conference
All deadlines are at the end of the day specified, anywhere on Earth (UTC-12).
Submission instructions
Workshop CLD2 follows all requirements of the ECAI 2024 main conference. Papers must be written in English, be prepared for double-blind review using the ECAI LaTeX template, and not exceed 7 pages (plus at most 1 extra page for references).
Excessive use of typesetting tricks to make things fit is not permitted. Please do not modify the style files or layout parameters.
Link to submission system coming soon.
Organization commitee
Paweł Zyblewski
Assistant Professor at the Department of Systems and Computer Networks, Wroclaw University of Science and Technology, Poland
Michał Woźniak
Professor of Computer Science at the Department of Systems and Computer Networks, Wroclaw University of Science and Technology, Poland.
Paweł Ksieniewicz
Associate Professor of Computer Science at the Department of Systems and Computer Networks, Wroclaw University of Science and Technology, Poland.