Knowledge Graph Feature Selection for Prediction Accuracy
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Solution Overview
Problem
Prediction engines in wireless communication systems face challenges in managing large amounts of data, determining important input features, and efficiently generating accurate predictions due to high processing requirements and difficulty in updating systems.
Innovation Solution
The implementation of a time-based knowledge graph that selects relevant features using historical data and machine learning interpretability models to input into prediction engines, reducing the number of features needed and improving prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a prediction engine processes a large amount of information to generate accurate predictions, then prediction accuracy is improved, but processing power and time requirements increase
Solution Approach 1:
The patent extracts and identifies only the most relevant features from a large dataset using machine learning interpretability models. The knowledge graph selectively pulls out critical features (such as specific network parameters, user behavior patterns, or device characteristics) that have the highest impact on prediction accuracy, while filtering out redundant or less important data. This extraction process maintains prediction accuracy by preserving essential information while reducing the overall data volume that requires processing.
Solution Approach 2:
The knowledge graph assigns different levels of importance or quality to different features based on their relevance to the prediction task. Rather than treating all input data uniformly, the system applies local quality by weighting certain features more heavily based on their contribution to accurate predictions. This allows the prediction engine to focus computational resources on high-value features while reducing processing of lower-priority data elements.
2Measurement precision
If a prediction system uses many input features to improve prediction accuracy, then prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The knowledge graph extracts only the essential features needed for accurate predictions from a comprehensive set of possible input variables. By identifying and isolating the most impactful features through machine learning interpretability analysis, the system reduces the number of input parameters fed into the prediction engine, thereby simplifying the overall system architecture while preserving predictive performance.
Solution Approach 2:
The patent segments the feature selection process into distinct stages: first, the knowledge graph identifies potential relevant features; second, machine learning interpretability models rank these features by importance; third, only the top-ranked features are passed to the prediction engine. This segmentation of the feature processing pipeline reduces system complexity by creating a structured, multi-stage approach rather than processing all features simultaneously.
3Measurement precision
If a prediction system processes comprehensive data to ensure accurate predictions, then prediction accuracy is improved, but resource management becomes more difficult
Solution Approach 1:
The knowledge graph extracts and identifies only the critical features required for accurate predictions, filtering out redundant or less important data elements. This selective extraction reduces the volume of information that the prediction system must manage, thereby simplifying resource allocation and reducing the computational overhead associated with processing and storing comprehensive datasets.
Solution Approach 2:
The system dynamically adjusts the parameters of feature selection based on the specific prediction task, available resources, and data characteristics. By changing parameters such as the number of features to process, the depth of analysis, or the threshold for feature importance, the knowledge graph enables the prediction system to optimize its resource consumption while maintaining accurate predictions across different operational contexts.
Data Source
AI summary
A device may include a processor configured to receive real-time data associated with a prediction usage system; determine one or more features associated with the received real-time data; select one or more relevant features, associated with a set of prediction output classes, based on the determined one or more features, using a knowledge graph for the set of prediction output classes; and provide the one or more relevant features as input to a prediction system for the set of prediction output classes. The processor may be further configured to obtain a prediction associated with the set of prediction output classes from the prediction system based on the provided one or more relevant features as input and provide the obtained prediction to the prediction usage system.