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4 Publications
2023 | Journal Article | LibreCat-ID: 48777
Fumagalli, F., Muschalik, M., Hüllermeier, E., & Hammer, B. (2023). Incremental permutation feature importance (iPFI): towards online explanations on data streams. Machine Learning. https://doi.org/10.1007/s10994-023-06385-y
LibreCat
| DOI
2023 | Journal Article | LibreCat-ID: 50262
Fumagalli, F., Muschalik, M., Hüllermeier, E., & Hammer, B. (2023). Incremental permutation feature importance (iPFI): towards online explanations on data streams. Machine Learning, 112(12), 4863–4903. https://doi.org/10.1007/s10994-023-06385-y
LibreCat
| DOI
2021 | Journal Article | LibreCat-ID: 25035
Haddenhorst, B., Bengs, V., & Hüllermeier, E. (2021). On testing transitivity in online preference learning. Machine Learning, 2063–2084. https://doi.org/10.1007/s10994-021-06026-2
LibreCat
| DOI
2018 | Journal Article | LibreCat-ID: 3402
Melnikov, V., & Hüllermeier, E. (2018). On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis. Machine Learning. https://doi.org/10.1007/s10994-018-5733-1
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