Decision Tree Leaf Node Statistical Representation for Parallel Execution
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Solution Overview
Problem
The inefficiency and high resource consumption in traversing and executing decision trees, particularly due to the need to access and store entire tree structures, which limits parallel execution and increases computational costs in applications like machine learning.
Innovation Solution
Representing leaf nodes with statistical information from historical entities, allowing for the determination of distance measures and prediction results without requiring the entire decision tree structure, enabling efficient storage and parallel execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the entire decision tree structure is accessed and stored for execution, then the prediction accuracy is maintained, but the storage requirements and computational resource consumption increase significantly
Solution Approach 1:
The patent extracts only the necessary information from the decision tree structure - specifically the leaf node representations containing statistical information about historical entities. Instead of storing and accessing the entire tree structure, only the condensed leaf node data is retained, which significantly reduces storage requirements while maintaining prediction capability through distance measure calculations.
Solution Approach 2:
The patent creates simplified copies of leaf nodes that contain statistical information (such as mean values, standard deviations, and prediction results) instead of copying the entire decision tree structure. These compressed representations serve as sufficient substitutes for making predictions, reducing the amount of data that needs to be stored and transmitted.
2Measurement precision
If the entire decision tree structure is traversed for prediction, then accurate prediction results are obtained, but the execution time and computational cost increase
Solution Approach 1:
The patent extracts only the essential statistical information from leaf nodes (mean values, standard deviations, prediction results) and removes the need to traverse the entire tree structure. This extraction allows for direct calculation of distance measures between target entity attributes and leaf node statistics, eliminating time-consuming tree traversal while maintaining prediction accuracy.
Solution Approach 2:
The patent performs preliminary computations by pre-calculating and storing statistical information (mean values, standard deviations) for each leaf node based on historical entities. This preliminary action enables faster real-time predictions, as the statistical characteristics are already computed and stored, eliminating the need for repeated tree traversals during prediction.
3Loss of information
If the entire decision tree structure is stored in memory, then complete information is available for prediction, but the memory consumption and resource usage increase
Solution Approach 1:
The patent extracts only the critical statistical information from the decision tree leaf nodes - specifically the mean values, standard deviations, and prediction results of historical entities. This extracted information is sufficient for making predictions through distance measure calculations, while significantly reducing the memory footprint compared to storing the complete tree structure.
Solution Approach 2:
The patent transforms the decision tree structure from a hierarchical node-based representation into a set of statistical parameters (mean values, standard deviations, prediction results) for each leaf node. This parameter transformation reduces the information to its essential numerical characteristics, enabling efficient storage and computation while preserving predictive capability.
4Ease of manufacture
If traditional decision tree execution is used, then the prediction logic is straightforward to implement, but parallel execution is limited and productivity decreases
Solution Approach 1:
The patent segments the prediction process into independent calculations for each leaf node. Instead of traversing a hierarchical structure where nodes are interdependent, each leaf node's distance measure can be calculated independently using its statistical parameters. This segmentation enables parallel execution across multiple processors or cores, significantly improving productivity while maintaining implementation simplicity through modular calculations.
Solution Approach 2:
The patent creates independent copies of leaf node statistical information that can be processed in parallel. Each leaf node representation is a self-contained unit with all necessary statistics for prediction, allowing multiple predictions to be performed simultaneously without requiring sequential tree traversal. This copying approach maintains implementation simplicity while enabling efficient parallel processing.
Data Source
AI summary
Embodiments of the present disclosure relate to a method, system, and computer program product for efficient execution of a decision tree. According to the method, respective target values of a plurality of attributes of a target entity are obtained. Representations of a plurality of leaf nodes of a decision tree are obtained. Each of the representations indicates respective statistic values of a plurality of attributes of historical entities and a statistic prediction result determined from historical prediction results output at a respective one of the plurality of leaf nodes for the historical entities. Distance measures between the target entity and the plurality of leaf nodes are determined based on the target values and the statistic values indicated by the representations of the plurality of leaf nodes. A target prediction result for the target entity is determined based on the distance measures and the statistic prediction results of the historical entities.