Multi-Dimensional Decision Tree Navigation Using Acceptable Alternative Nodes
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Solution Overview
Problem
Navigating multi-dimensional decision trees is challenging due to the potential suboptimality of selecting child nodes based solely on the highest or lowest attribute value, as this may not consider the optimal choice for other attributes.
Innovation Solution
The system uses 'acceptable alternative child nodes' defined by an acceptance delta parameter to select an end child node, considering multiple attribute values and allowing for navigation paths that account for variations in attribute values, thereby improving the decision-making process in multi-dimensional decision trees.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a greedy algorithm selects child nodes based solely on the highest or lowest attribute value, then the decision process is simple and fast, but the decision path may be suboptimal when multiple attributes are considered
Solution Approach 1:
The patent changes the selection criterion from a single extreme value (highest or lowest) to a range of acceptable values defined by the acceptance delta parameter. This allows the system to consider multiple child nodes that fall within the acceptable range, thereby improving decision path optimality while maintaining computational efficiency through a modified greedy approach.
Solution Approach 2:
The patent introduces a new dimension to the selection process by adding the acceptance delta parameter, which creates a value range rather than selecting based on a single point value. This dimensional expansion allows the system to evaluate multiple attributes simultaneously and select child nodes that optimize overall decision paths rather than individual attribute extremes.
2Measurement precision
If the acceptance delta parameter is set to a small value, then the selection is more precise for the first attribute, but fewer alternative child nodes are considered
Solution Approach 1:
The acceptance delta parameter is designed to be dynamic rather than fixed, allowing the system to adjust the range of acceptable values based on the specific context and requirements. This dynamic adjustment enables the system to balance between precision and the number of alternatives considered, optimizing the trade-off between measurement precision and adaptability.
Solution Approach 2:
The patent applies partial action by considering only child nodes that fall within the acceptable delta range rather than evaluating all possible child nodes. This partial evaluation approach maintains precision while reducing computational overhead, and can be adjusted to consider more or fewer alternatives as needed.
Data Source
AI summary
A decision tree analysis system and method for navigating a multi-dimensional decision tree uses acceptable alternative child nodes of a target child node to select an end child node for a parent node, where the parent node and the final child node define a single step of a navigation path for the multi-dimensional decision tree. The acceptable alternative child nodes are based on an acceptance delta parameter for a particular attribute, which defines a value range about an attribute value of the target child node within which a child node is determined to be an acceptable alternative child node of the target child node.