Fuzzy Linguistic Summaries as Explanations for Partialy Labeled Time Series

Katarzyna Kaczmarek-Majer

Department of Stochastic Methods Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland


Knowledge extraction from sensor data in healthcare is still limited, primarily due to the underexplored potential of datasets lacking proper annotations. Semi-supervised fuzzy clustering emerges as a promising approach for explaining evolving data streams and capturing the information about their hidden structure. Positioned between supervised and unsupervised learning, semi-supervised learning holds significant promise. We will also explain and verify how to assess the impact of partial supervision properly and what are its consequences. The proposed approach combines theoretical aspects of semi-supervised learning from partially-labeled sensor data with fuzzy linguistic summarization. Linguistic summarization belongs to the class of data-to-text approaches. We construct linguistic summaries for the partially labelled data streams, and the drifts in data streams are reflected in the construction of linguistic variables. The proposed approach enables to summarize of large data streams into meaningful and human-consistent information granules. Finally, a case study in smartphone-based mental health monitoring is presented. Acoustic features of speech are promising as objective markers for mental health monitoring. Specialized smartphone apps can gather such acoustic data without disrupting the daily activities of patients. Nonetheless, the psychiatric assessment of the patient’s mental state is typically a sporadic occurrence that takes place every few months. Consequently, only a slight fraction of the acoustic data is labelled and applicable for supervised learning. Numerical experiments for real-life and simulated data illustrate the performance of the proposed method.


Wykład odbędzie się 14 marca 2024 o godzinie 17.00 przy użyciu komunikatora Zoom.