M-CaStLe generalizes local stencil-based causal discovery to the multivariate case and decomposes resulting graphs into reaction and spatial components for interpretation in space-time gridded data.
Conditional permutation importance revisited.BMC Bioinformatics, 21(1):307, 2020
2 Pith papers cite this work. Polarity classification is still indexing.
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Conditional inference forests rank competitively as top-k feature selectors in classification and regression benchmarks, with runtime factors identified but limited impact on scores.
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M-CaStLe: Uncovering Local Causal Structures in Multivariate Space-Time Gridded Data
M-CaStLe generalizes local stencil-based causal discovery to the multivariate case and decomposes resulting graphs into reaction and spatial components for interpretation in space-time gridded data.
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Conditional Inference Trees and Forests for Feature Selection
Conditional inference forests rank competitively as top-k feature selectors in classification and regression benchmarks, with runtime factors identified but limited impact on scores.