76 citations · 168 across the 6 of their papers we have counts for
9 papers
Explaining machine learning models for age classification in human gait analysis
Djordje Slijepcevic, Fabian Horst, Marvin Simak +7
Machine learning (ML) models have proven effective in classifying gait analysis data, e.g., binary classification of young vs. older adults. ML models, however, lack in providing h…
Explaining automated gender classification of human gait
Fabian Horst, Djordje Slijepcevic, Matthias Zeppelzauer +6
State-of-the-art machine learning (ML) models are highly effective in classifying gait analysis data, however, they lack in providing explanations for their predictions. This "blac…
On the Explanation of Machine Learning Predictions in Clinical Gait Analysis
Djordje Slijepcevic, Fabian Horst, Sebastian Lapuschkin +6
Machine learning (ML) is increasingly used to support decision-making in the healthcare sector. While ML approaches provide promising results with regard to their classification pe…
Automatic Classification of Functional Gait Disorders
Djordje Slijepcevic, Matthias Zeppelzauer, Anna-Maria Gorgas +5
This article proposes a comprehensive investigation of the automatic classification of functional gait disorders based solely on ground reaction force (GRF) measurements. The aim o…
Clustering of imbalanced high-dimensional media data
Sarka Brodinova, Maia Zaharieva, Peter Filzmoser +2
Media content in large repositories usually exhibits multiple groups of strongly varying sizes. Media of potential interest often form notably smaller groups. Such media groups dif…
Robust and sparse k-means clustering for high-dimensional data
Sarka Brodinova, Peter Filzmoser, Thomas Ortner +2
In real-world application scenarios, the identification of groups poses a significant challenge due to possibly occurring outliers and existing noise variables. Therefore, there is…