Arithmetical Binary Decision Tree Traversals
This work addresses a specific computational challenge in decision tree processing, but appears incremental as it builds on existing traversal techniques with arithmetic enhancements.
The paper tackles the problem of traversing binary decision trees by introducing methods that use arithmetic operations and novel representation matrices to flatten tree structures and embed node tests into binary vectors, resulting in an approach based on maximum inner product search.
This paper introduces a series of methods for traversing binary decision trees using arithmetic operations. We present a suite of binary tree traversal algorithms that leverage novel representation matrices to flatten the full binary tree structure and embed the aggregated internal node Boolean tests into a single binary vector. Our approach, grounded in maximum inner product search, offers new insights into decision tree.