Interactive Machine Learning with Evolving Fuzzy Systems

The major goal of this project is to develop a new methodological framework for overcoming the current limitations of on-line machine learning (ML) systems in industrial installations, social media platforms, health-care systems, web mining tools, predictive maintenance frameworks etc. Currently, ML systems are mostly oriented more on a precise on-line processing functionality where continuously arriving data streams are processed and high-qualitative models are learnt from them for various purposes such as decision support, forecasts of states, classifications, quality control etc. Indeed, outputs of these learning processes and/or internal model building stages may be shown to the user, but this is basically restricted within a passive supervision frontend, at most allowing some rudimentary feedback by human users (‘cold interaction’). However, current systems do not foresee an advanced interaction and communication methodology, where the human is stimulated and would be thus willing and able to bring in her/his knowledge about the process (e.g., due to her/his past experience), e.g., by actively defining newly arising events, relations or by modifying several parts of the models in the ML system in case of (severe) drifts or model performance deteriorations. It is expected that, within an advanced interactive system, both, humans and machines, benefit from each other, achieving knowledge gains for humans as well as performance boosts for the ML models likewise.