Dynamic Depth Classification in Tree-Structure Inference Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for classifying objects using tree-structure models, such as decision trees, require reconstructing inference models each time the classification depth changes, which is time-consuming and computationally costly.
Innovation Solution
An information processing method and apparatus that classifies objects by setting depth information to determine which node groups to traverse in a tree structure, allowing for dynamic adjustment of classification depth without generating a new inference model each time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If inference models are reconstructed each time classification depth changes, then classification accuracy can be optimized for each user, but processing time and computational cost increase significantly
Solution Approach 1:
The patent pre-generates multiple inference models with different classification depths in advance, before they are actually needed. These pre-generated models are stored and can be quickly selected and applied based on user requirements, eliminating the need for time-consuming model reconstruction at runtime.
Solution Approach 2:
The patent creates a dynamic model selection mechanism that allows the system to adaptively choose the appropriate pre-generated inference model based on the current classification depth requirements. This dynamic selection approach maintains high classification accuracy while avoiding the time cost of generating models on-demand.
2Adaptability or versatility
If inference models are reconstructed each time classification depth changes, then classification can be tailored to user needs, but computational cost increases
Solution Approach 1:
Multiple inference models with different classification depths are pre-generated and stored in advance. When a user requires classification at a specific depth, the system simply selects the appropriate pre-generated model rather than reconstructing it, significantly reducing computational cost while maintaining user-specific adaptability.
Solution Approach 2:
The patent creates multiple copies of inference models with different depths (shallow copy, medium copy, deep copy) that can be stored and reused. These model copies allow the system to serve different user needs without performing expensive reconstruction operations, reducing computational overhead while maintaining versatility.
3Measurement precision
If deep classification is performed, then detailed classification results are obtained, but processing time increases
Solution Approach 1:
The patent implements a dynamic model selection strategy that matches the classification depth requirement with the appropriate pre-generated model. When detailed classification is needed, the deep inference model is selected; when faster processing is needed, shallower models are chosen. This dynamic adaptation maintains classification detail when necessary while improving processing speed when possible.
Solution Approach 2:
The system changes the depth parameter of the inference model based on requirements. By having pre-generated models with different depth parameters (shallow, medium, deep), the system can adjust the classification detail level without reconstructing models, balancing processing detail and speed based on specific needs.
Data Source
AI summary
An information processing method in which an object of interest is classified using node group information defining a node group having modeled a scheme of classification as a tree structure and having grouped nodes possessing a same parent node, comprises: setting depth information for determining whether to perform classification for a particular node group when sequentially traversing node groups from the parent node using the node group information to classify the object of interest; and classifying the object of interest by sequentially traversing node groups from the parent node using the node group information, and providing a classification result, wherein classifying the object of interest varies a depth up to which node groups are sequentially traversed from the parent node to classify the object of interest, in accordance with setting of the depth information.