Machine Learning Quality Scores for Connected Components
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Solution Overview
Problem
Current methods lack an efficient and accurate way to automatically measure quality scores for connected components using machine learning models, particularly in complex network representations, which hinders the identification of high-quality data and user identities.
Innovation Solution
A system employing machine learning models, such as deep neural networks and decision trees, to generate linkage scores between nodes based on their similarity and association, forming connected components by linking nodes that exceed a predetermined threshold, and calculating quality scores using precision and recall metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to measure quality scores for connected components, then the process is simpler, but the accuracy and automation level are insufficient
Solution Approach 1:
The patent replaces traditional manual or rule-based quality assessment methods with machine learning models (including deep neural networks and gradient boosting machines) that automatically analyze connected component data. This substitution of mechanical/manual processes with intelligent algorithms directly improves measurement precision while the automated nature eventually reduces operational complexity despite initial system setup requirements.
Solution Approach 2:
The patent introduces machine learning models as intermediary components between raw connected component data and quality score outputs. These models serve as intelligent mediators that process complex relationships and patterns in the data, enabling accurate quality measurement without requiring direct manual assessment or simple rule-based systems.
2Productivity
If manual methods are used to assess connected component quality, then the system is easier to implement, but the productivity and scalability are limited
Solution Approach 1:
The patent implements self-service through automated machine learning models that independently assess quality scores for connected components without requiring manual intervention. The system processes data autonomously, generating quality measurements at scale, which dramatically improves productivity and eliminates time losses associated with manual assessment while maintaining ease of operation through automated workflows.
3Reliability
If simple quality metrics are used, then the calculation is faster, but the ability to identify true user identities is reduced
Solution Approach 1:
The patent transforms the quality assessment by changing from simple metrics to comprehensive machine learning-based evaluation parameters. The system uses multiple features including linkage scores, precision, and recall metrics processed through trained models, which significantly improve reliability of user identity identification. The automated calculation processes these enhanced parameters efficiently, minimizing time loss despite the increased analytical depth.
Data Source
AI summary
A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform: generating linkage scores between nodes at least based on a machine learning model; creating links between the nodes to form connected components based on the linkage scores exceeding a predetermined threshold; generating an actual matching linkage set of the nodes linked in the connected components by using a relaxed blocking criteria; and generating a quality score for the connected components. Other embodiments are disclosed.