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


Rare event detection - Principles

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

Methods and evaluation

This section talks about bla, bla

Coffee Break

Rare event detection in classified data

This section talks about bla, bla

Explanation of rare events

This section talks about bla, bla

Fraud Detection in Telco - a case study

This section talks about bla, bla

Open Challenges

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




Luis Torgo

Canada Research Chair and Professor of Computer Science


Stan Matwin

Canada Research Chair and Professor


Nuno Moniz

Post Doctoral Fellow and Invited Professor


Paula Branco

Assistant Professor, U. Ottawa


Rita Ribeiro

Assistant Professor


Lubomir Popelinsky

Associate Professor