Graph-Based Predictive Classifications for Entity Taxonomies
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Solution Overview
Problem
Conventional classification techniques face challenges in generating predictive inferences in robust predictive domains due to limitations in taxonomies, which can lead to misclassification errors and fail to account for high variance and heterogeneity.
Innovation Solution
The use of graph-based approaches to generate empirical domain taxonomies, which break down entity classes into multiple subclasses based on homogeneous behavior, allowing for a granular predictive classification tailored to actual entity interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional classification techniques use broad taxonomies to group entities, then classification simplicity is maintained, but misclassification errors increase and predictive accuracy deteriorates
Solution Approach 1:
The patent segments broad entity classes into multiple fine-grained subclasses by generating graph-based empirical taxonomies. Each entity class is divided into subclasses based on homogeneous behavior patterns, transforming a single broad category into multiple specialized subcategories that improve classification precision while managing complexity through systematic graph-based methods
Solution Approach 2:
The patent applies local quality by tailoring classification to individual entities through graph-based representations. Each entity receives a customized classification based on its specific interaction patterns and behavior, rather than applying uniform broad categorization, thereby improving local classification accuracy for each entity while maintaining overall system coherence
2Ease of operation
If entities self-designate their taxonomy code, then classification process is simplified, but misclassification errors increase due to lack of objective validation
Solution Approach 1:
The patent implements feedback by using graph-based empirical taxonomies to objectively validate and correct self-designated taxonomy codes. The system compares entity behavior patterns against graph-derived classifications, providing feedback that identifies and corrects misclassifications, thereby improving reliability while maintaining operational simplicity through automated validation
Solution Approach 2:
The patent applies preliminary action by pre-generating graph-based empirical taxonomies that encode correct classification patterns before entities need classification. These pre-computed graph representations serve as reference standards that objectively validate self-designated codes, preventing misclassification errors before they affect predictive outcomes
3Adaptability or versatility
If traditional taxonomies are used to account for high variance and heterogeneity, then broader coverage is achieved, but information value degradation occurs and misclassification errors increase
Solution Approach 1:
The patent introduces another dimension by adding graph-based interaction patterns as a new classification dimension alongside traditional taxonomy codes. This multi-dimensional approach captures heterogeneity and variance that single-dimension taxonomies miss, improving adaptability while preserving information value through complementary classification perspectives
Solution Approach 2:
The patent applies composite materials by combining traditional taxonomy classifications with graph-based empirical classifications into a hybrid classification system. This composite approach integrates multiple classification sources, achieving broader coverage of variance and heterogeneity while preventing information degradation through the complementary strengths of each classification method
Data Source
AI summary
Various embodiments of the present disclosure provide graph-based techniques for generating granular predictive classifications for entities in a predictive domain. The graph-based techniques include generating a network graph for an entity or entity class based on a plurality of interaction data objects for the entity. The network graph includes a plurality of nodes and a plurality of edges. Each node corresponds to a particular interaction code of at least one of the plurality of interaction data objects. Each edge connects a node pair that is associated with a particular interaction data object. The nodes and edges are weighted to enable the clustering of the network graph for an entity class. An entity network graph may be compared to node clusters of a class network graph to generate a behavior based predictive classification.