Session

Friday, June 23, 2017 - 09:00 to 18:45
Studying the Potential of Multi-Target Classification to Characterize Combinations of Classes with Skewed Distribution
Abstract: 
The identification of subpopulations with particular characteristics with respect to a disease is important for personalized diagnostics and therapy design. But what if the manifestation of the disease is not described by one target variable but of many? Multi-target classification algorithms are the straightforward choice in this context and have been successfully applied in different application scenarios. However, most investigations do not focus on the effects of a skewed class distribution, where the prevalence of one of the multi-target combinations is more rare than the others. Moreover, in personalized medicine, it is not only essential to separate subpopulations but also to characterize them in a human understandable way. In this study, we analyze the potential of multi-target classification for the identification and characterization of subpopulations, that exhibit higher prevalence for a rare combination of targets. We report on the results of our approach for the analysis of tinnitus screening data with respect to two target variables, tinnitus loudness and handicap.
Arne Schneck's picture
Arne Schneck
Myra Spiliopoulou's picture
Myra Spiliopoulou
Otto-von-Guericke University Magdeburg (DE)
Sven Kalle's picture
Sven Kalle
Rudiger Pryss's picture
Rudiger Pryss
Ulm University (DE)
Winfried Schlee's picture
Winfried Schlee
Thomas Probst's picture
Thomas Probst
Berthold Langguth's picture
Berthold Langguth
Manfred Reichert's picture
Manfred Reichert
Michael Landgrebe's picture
Michael Landgrebe