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.

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.

Workshop program

CLD2, as a half-day event, will consist of two 90-minute sessions, lasting from 9:00 to 10:30 am and from 11:00 to 12:30 pm separated by a 30-minute coffee break.

Session 1 (9:00–10:30)

Session 2 (11:00–12:30)

Keynote talk

Employing modality encoding techniques for difficult data classification

Paweł Zyblewski is an Assistant Professor at the Department of Systems and Computer Networks, Wroclaw University of Science and Technology, Poland. Research work related to his doctoral dissertation, entitled "Classifier selection for imbalanced data stream classification", in which he focused on the use of dynamic ensemble selection algorithms for the analysis of highly imbalanced data streams, resulted in him receiving a Scholarship from the Minister of Education and Science for outstanding young scientists and winning the Polish Artificial Intelligence Society (PSSI) Best Ph.D. Dissertation in Artificial Intelligence Contest in 2021. His research interests are currently related to imbalanced data classification, data stream analysis, multimodal data analysis, modality encoding, and semi-supervised learning.

The talk will include a brief presentation of the applicability of modality encoding methods – including the transformation of tabular data and text to discrete digital signals as well as sonification – in the classification of streaming and multimodal data.

Paweł Zyblewski
Wrocław University of Science and Technology

Topics of interest

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 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.

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.

All deadlines are at the end of the day specified, anywhere on Earth (UTC-12).

Submission instructions and conference proceedings

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.

Conference proceedings will be publised The Proceedings of Machine Learning Research series.

Organization commitee

We’re researchers from Department of Systems and Computer Networks, which since 25 years conducts fundamendal research on Machine Learning models in difficult scenarios. We are from Poland.

  • Paweł Zyblewski

    Assistant Professor 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.

  • Michał Woźniak

    Professor of Computer Science at the Department of Systems and Computer Networks, Wroclaw University of Science and Technology, Poland.

Program commitee

The submission will be reviewed by an international group of experts in the field of artificial intelligence.