system structure in data-driven predictive analytics
July 2022 at 3pm (UK time)
Current predictive analytics approaches are strongly focused
on optimizing accuracy metrics, leaving little room to incorporate a priori
knowledge about the processes under analysis and relegating to a secondary
concern the interpretation of results. However, in the analysis of complex
systems, one of the main interests is precisely the induction of relevant
associations, in order to understand or clarify the way systems operate. On the
other hand, there is often information available regarding the structure of the
processes, which could be used in benefit of the analysis and to enhance the
interpretation of results.
The importance of this issue is not new and has motivated the
development of multiblock approaches that try to improve the interpretation of
results, while maintaining the quality of predictions. In this talk, two
classes of multiblock frameworks are addressed, that present
interpretational-oriented features, while allowing some system structure to be
incorporated. One class is based on the existence of a priori knowledge for
building the blocks of variables, while the other is able to extract the system
structure in a data-driven way.
This webinar will be presented by Marco Reis, University of Coimbra, Portugal.
The webinar will last no longer than one hour.
To register please contact firstname.lastname@example.org