Visual Human+Machine Learning

In this paper we describe a novel method to integrate interactive visual analysis and machine learning to support the insight generation of the user. The suggested approach combines the vast search and processing power of the computer with the superior reasoning and pattern recognition capabilities of the human user. An evolutionary search algorithm has been adapted to assist in the fuzzy logic formalization of hypotheses that aim at explaining features inside multivariate, volumetric data. Up to now, users solely rely on their knowledge and expertise when looking for explanatory theories. However, it often remains unclear whether the selected attribute ranges represent the real explanation for the feature of interest. Other selections hidden in the large number of data variables could potentially lead to similar features. Moreover, as simulation complexity grows, users are confronted with huge multidimensional data sets making it almost impossible to find meaningful hypotheses at all....

• Type: Conference
• Year: 2009
• Where: VIS
• Authors: Raphael Fuchs, Jürgen Waser, Meister Eduard Gröller
• Keywords: Generated Hypotheses, Initial Hypotheses, Meaningful Hypotheses, VIS 2009, Visualization