Feature Set Selection Using Metagradients for Dataset Generation
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Solution Overview
Problem
Finding correlations or appropriate datasets among a large number of diverse datasets is computationally infeasible due to combinatorial explosion, making it difficult to assess each combination effectively.
Innovation Solution
A computer system uses a machine learned feedback loop to iteratively select feature sets based on metagradient information, employing a hierarchical graph search and optimization techniques to identify a subset of features that meet a given threshold, thereby addressing the combinatorial explosion issue.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all possible combinations of features from hundreds or thousands of diverse datasets are assessed, then the most relevant feature sets for machine learning can be identified, but the computational complexity becomes infeasible due to combinatorial explosion
Solution Approach 1:
The patent segments the overwhelming set of all possible feature combinations into manageable subsets using hierarchical clustering. Features are organized into clusters based on similarity, and the system evaluates representative subsets from each cluster rather than all individual combinations, dramatically reducing computational complexity while maintaining selection accuracy.
Solution Approach 2:
The patent performs preliminary feature engineering and selection before the main machine learning model training. By pre-processing and pre-selecting features using domain knowledge and automated techniques, the system reduces the dimensionality of the problem space beforehand, making subsequent model training computationally feasible.
2Loss of information
If a large number of diverse datasets are processed to find correlations, then more comprehensive insights can be obtained, but the time required for data processing and analysis increases significantly
Solution Approach 1:
The patent applies partial action by selectively processing only the most relevant datasets and features identified through automated feature selection, rather than exhaustively analyzing all available data. This approach maintains information completeness for critical features while significantly reducing processing time by excluding irrelevant data.
Solution Approach 2:
The patent replaces manual data processing and feature selection with automated machine learning algorithms. The system uses algorithms to automatically identify correlations, select features, and process diverse datasets, substituting computational automation for manual analysis and dramatically reducing processing time.
3Manufacturing precision
If manual feature selection is performed to ensure quality, then the selected features meet higher standards, but the process becomes labor-intensive and difficult to scale
Solution Approach 1:
The patent implements self-service by enabling the system to automatically perform feature selection and evaluation without manual intervention. The machine learning algorithms autonomously assess feature quality, identify correlations, and select optimal feature sets, maintaining high quality standards while achieving full automation and scalability.
Solution Approach 2:
The patent incorporates feedback mechanisms where the machine learning model evaluates selected features and provides feedback on their performance. This feedback loop allows the system to iteratively improve feature selection quality, automatically adjusting selections based on model performance metrics while maintaining high standards without manual oversight.
Data Source
AI summary
A computer system is provided that is programmed to select feature sets from a large number of features. Features for a set are selected based on metagradient information returned from a machine learning process that has been performed on an earlier selected feature set. The process can iterate until a selected feature set converges or otherwise meets or exceeds a given threshold.