Path-based adaptive weighting of random forest trees via decision path patterns delivers statistically significant accuracy gains on 36 binary classification benchmarks with minimal class-recall regression.
Pattern recognition 41(5), 1718–1731 (2008)
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.LG 2verdicts
UNVERDICTED 2representative citing papers
Proposes ISensD and ESensI methods to increase robustness of multi-sensor EO models to missing sensors, with experiments on three temporal datasets showing ensemble models are most robust.
citing papers explorer
-
Decision-Path Patterns as Tree Reliability Signals: Path-based Adaptive Weighting for Random Forest Classification
Path-based adaptive weighting of random forest trees via decision path patterns delivers statistically significant accuracy gains on 36 binary classification benchmarks with minimal class-recall regression.
-
Increasing the Robustness of Model Predictions to Missing Sensors in Earth Observation
Proposes ISensD and ESensI methods to increase robustness of multi-sensor EO models to missing sensors, with experiments on three temporal datasets showing ensemble models are most robust.