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  • CIS
    Members: Free
    IEEE Members: Free
    Non-members: Free
    Length: 00:57:44
16 Nov 2022

Nowadays, ever more data is collected, for instance in the healthcare domain. The amount of patients� data has doubled in the previous two years. This exponential growth creates a data flood that is hard to handle by decision makers. In many domains, humans are collaborating with machines for decision making purposes to cope with the resulting data complexity and size. This collaboration can be realized through machine learning, visual analytics, or online analytical processing, where a machine is just a tool � but often used to take important decisions. The question now is: do we really understand the data using the tool this way?

Explainability is a great challenge in data analytics, with the aim to explain to the user why certain decisions have been recommended or made. The challenge is how to make data more understandable for humans? Fuzzy techniques, or the application of the computing with words paradigm has the potential to close the gap by using natural language as the communication means. In this talk I focus on descriptive analytics and show with a set of examples how fuzzy techniques can provide better insight of data to the user. I pay special attention to the technique of linguistic summaries. I will discuss the status quo of this technique, as well as new opportunities lying ahead.

Bio: Anna Wilbik is currently Professor in Data Fusion and Intelligent Interaction in the Department of Advanced Computing Sciences of Maastricht University, in the Netherlands. Currently she is also a chair of The Fuzzy Systems Technical Committee (FSTC) within IEEE CIS. She received her PhD (with honors) in Computer Science from the Systems Research Institute, Polish Academy of Science, Warsaw, Poland, in 2010. In 2011, she was a Post-doctoral Fellow with the Department of Electrical and Computer Engineering, University of Missouri, Columbia, USA. Anna is an alumnus of the Stanford University TOP500 Innovators: Science - Management - Commercialization Program.