LGAug 9, 2021
Collapsing the Decision Tree: the Concurrent Data Predictor
arXiv:2108.03887v14 citations
Originality Incremental advance
AI Analysis
This work addresses a bottleneck in decision tree classifiers for machine learning applications, but it appears incremental as it modifies an existing method without a paradigm shift.
The paper tackled the problem of sequential attribute evaluation in decision trees by proposing a concurrent data predictor that collapses the tree into a flat structure, resulting in improved prediction accuracy.
A family of concurrent data predictors is derived from the decision tree classifier by removing the limitation of sequentially evaluating attributes. By evaluating attributes concurrently, the decision tree collapses into a flat structure. Experiments indicate improvements of the prediction accuracy.