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Context-Aware Knowledge Discovery: Opportunities, Techniques and Applications

Tutorial at The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery ECML-PKDD 2016
Riva del Garda, Italy, September 19–23, 2016.


Cesar Ferri, [Peter Flach] (, [Meelis Kull] ( , Nicolas Lachiche


Traditionally, knowledge discovery aims to learn patterns from given data that are expected to apply to future data. Often there is relevant contextual information that, although it can have a considerable effect on the quality of the results, is rarely taken into account as it is not directly represented in the training data. This tutorial aims to elucidate the role of context and context changes in the knowledge discovery process, and to demonstrate how recent research advances in context-aware machine learning and data mining can be put to practical use. The tutorial will cover the main types of context changes, including changes in costs, data distribution and others. Participants will develop basic skills in choosing the appropriate modelling techniques and visualisation tools for the construction, selection, adaptation and understanding of versatile and context-aware models. We will discuss how data mining methodologies such as CRISP-DM can be extended to take context change into account. The tutorial will not only equip the attendees with new technical and methodological knowledge, but also encourage an anticipatory attitude towards context change.

##Slides Link


Covariate shift example


Cost Curves in R

[Calibration in R] (

[Calibration in Python] (

[Precision-Recall-Gain curves] (

[Optimal binary threshold selection] ( in [] (