Learning with Imbalanced Domains and Rare Event Detection

LIDTA 2020 is a half day tutorial to be held at ECML/PKDD’2020. This tutorial is about Learning with Imbalanced Domains and Rare Event Detection. It targets both newcomers on the subject but also researchers/professionals with previous experience.

Many real-world data-mining applications involve obtaining and evaluating predictive models using data sets with strongly imbalanced distributions of the target variable. Frequently, the least-common values of this target variable are associated with rare events that are highly relevant for end-users. Examples include many diverse domains, such as diagnosis of rare diseases, intrusion detection or popularity prediction in social media. Tackling the issues raised by imbalanced domains is crucial to both academia and industry.

This tutorial clearly describes the full pipeline for rare event detection. This includes i) the fundamentals and principles, ii) methods and evaluation, iii) rare events detection in classified data, iv) explanation, v) a case study on fraud detection in data streams and vi) open challenges.

Tutorial Schedule

Learning with Imbalanced Domains and Rare Event Detection - principles

In this part we will talk about the principles behind imbalanced domains and the detection of rare events

Performance Metrics for Learning with Imbalanced Domains and Rare Event Detection with Unsupervised Methods

This part of the tutorial talks about performance metrics in the context of imbalanced domains, and rare events detection using unsupervised methods

Rare Event Detection with Semi-supervised Methods, Class-based Anomaly Detection and Explanation of Rare Events

This part of the tutorial talks about semi-supervised approaches to rare event detection; class-based anomaly detection; and also about the topic of explanation of rare events

Learning with Imbalanced Domains and Rare Event Detection - Open Challenges

This part of the tutorial involves a general discussion on open challenges in this area

Organizers

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Luis Torgo

Canada Research Chair and Professor of Computer Science

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Stan Matwin

Canada Research Chair and Professor

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Nuno Moniz

Post Doctoral Fellow and Invited Professor

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Paula Branco

Assistant Professor, U. Ottawa

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Rita Ribeiro

Assistant Professor

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Lubomir Popelinsky

Associate Professor

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