141 citations · 597 across the 22 of their papers we have counts for
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cs.LG2023
Shapley Chains: Extending Shapley Values to Classifier Chains
Célia Wafa Ayad, Thomas Bonnier, Benjamin Bosch +1
In spite of increased attention on explainable machine learning models, explaining multi-output predictions has not yet been extensively addressed. Methods that use Shapley values…
cs.LG2023
Transferable Deep Metric Learning for Clustering
Simo Alami. C, Rim Kaddah, Jesse Read
Clustering in high dimension spaces is a difficult task; the usual distance metrics may no longer be appropriate under the curse of dimensionality. Indeed, the choice of the metric…
cs.LG2023
Chains of Autoreplicative Random Forests for missing value imputation in high-dimensional datasets
Ekaterina Antonenko, Jesse Read
Missing values are a common problem in data science and machine learning. Removing instances with missing values can adversely affect the quality of further data analysis. This is…