Year: 2025
Type: conference-paper
Abstract: Continuous monitoring of physiological signals requires models that learn online, adapt to concept drift, and remain interpretable for clinical decision support [1], [4], [5]. Electroencephalography (EEG) is widely used in healthcare for applications such as seizure detection and fatigue monitoring, where real-time analysis enables timely intervention. Ensemble methods achieve strong accuracy on data streams [9]–[11], whereas Formal Concept Analysis (FCA) provides transparent concept rules and has demonstrated competitive performance in batch settings [2], [14]. We introduce On lin eFCA, a weighted nominal-concept classifier that extends FCA to streaming conditions through a two-phase process: a grace period for initialization followed by incremental updates with weighted sampling and rejection handling. The method is implemented in the Massive Online Analysis (MOA) framework [3], ensuring reproducibility and direct comparison with existing stream learners. Experiments on the EEG Eye State dataset demonstrate that OnlineFCA outperforms baselines such as Naive Bayes, kNN, and Hoeffding Tree [8], while remaining below optimized ensembles such as Adaptive Random Forest [9]. Crucially, OnlineFCA produces explicit and human-readable rules, providing interpretability that is essential for real-time clinical monitoring. These findings highlight the promise of FCA-based classifiers for medical data streams, where transparent decision support is as critical as predictive accuracy [14].
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4415319269
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