Proposes the first algorithm for optimal hypersurface decision trees with time complexity O(K! × N^{DG+G}) plus pruning and incremental checks that reduce practical cost.
Tabular data: Deep learning is not all you need
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
LightGBM with surrounding-grid feature selection and Tweedie loss for precipitation yields lower RMSE than raw MSM forecasts, MSMG, and some CNN baselines across many Japanese locations and lead times.
citing papers explorer
-
Optimal hypersurface decision trees
Proposes the first algorithm for optimal hypersurface decision trees with time complexity O(K! × N^{DG+G}) plus pruning and incremental checks that reduce practical cost.
-
Improvements to the post-processing of weather forecasts using machine learning and feature selection
LightGBM with surrounding-grid feature selection and Tweedie loss for precipitation yields lower RMSE than raw MSM forecasts, MSMG, and some CNN baselines across many Japanese locations and lead times.