Compact Decision Tree Matrices for Efficient Machine Learning Processing
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Solution Overview
Problem
Decision trees used in machine learning become too large and variable, making them difficult to process and analyze effectively.
Innovation Solution
A system is introduced to generate a compact tree representation model in the form of a matrix design, such as an adjacency matrix, to maintain the relationships expressed by the decision tree structure, reducing the size and complexity for efficient processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If decision trees are used in their native form for machine learning, then they can represent complex decisions and outcomes, but they become too large and variable making them difficult to process and analyze
Solution Approach 1:
The patent transforms the decision tree from its traditional hierarchical structure into a matrix representation, changing the fundamental parameter of data structure. This matrix form maintains the decision logic while reducing size and improving processability, directly resolving the contradiction between representation capability and complexity
Solution Approach 2:
The patent creates a compact matrix copy of the decision tree that preserves the essential decision-making relationships. Instead of using the full native tree structure, a simplified matrix representation is generated that captures the same logic in a more compact form, reducing size while maintaining functionality
2Productivity
If compact tree representation is generated to reduce data size, then transmission and processing efficiency improve, but the complexity of generating the compact representation increases
Solution Approach 1:
The system performs preliminary transformation of decision trees into compact matrix representations before they are transmitted or processed by machine learning models. This advance preparation eliminates the need for complex real-time compression during transmission or processing, improving overall efficiency despite the initial transformation complexity
Data Source
AI summary
Aspects of the present disclosure involve systems, methods, devices, and the like for generating compact tree representations applicable to machine learning. In one embodiment, a system is introduced that can retrieve a decision tree structure to generate a compact tree representation model. The compact tree representation model may come in the form of a matrix design to maintain the relationships expressed by the decision tree structure.