Semantic Feature Crossing to Reduce Manual Machine Learning Configuration
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Solution Overview
Problem
Existing machine learning models face challenges in achieving better performance due to the manual configuration of feature crossing, which is time-consuming and not always effective, especially as the number of extracted features increases.
Innovation Solution
The proposed solution involves an automated feature crossing system that utilizes semantic knowledge about feature categories to determine semantic correlation relationships. This system classifies features into categories and applies feature crossing based on strong semantic correlations, allowing for automatic implementation of feature crossing without the need for deep learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual feature crossing configuration is used, then model performance can be improved, but time consumption and complexity increase significantly
Solution Approach 1:
The system performs automatic feature crossing by having the machine learning system itself determine which features to cross based on data analysis, eliminating the need for manual configuration. The system automatically identifies feature correlations and generates crossed features without human intervention, resolving the time consumption issue while maintaining performance improvements.
Solution Approach 2:
The system changes the approach from manual parameter selection to automated parameter determination by analyzing data characteristics. It dynamically identifies which features should be crossed based on statistical correlations and data patterns, transforming the feature crossing process from a manual task to an automated data-driven process that reduces time consumption while preserving model performance.
2Reliability
If the number of extracted features increases, then model performance can be improved, but the complexity of feature crossing configuration increases
Solution Approach 1:
When the number of extracted features increases, the system automatically manages the complexity by having the machine learning system itself determine which features to cross. It analyzes correlations among all available features and automatically generates the appropriate crossed features, eliminating the need for manual configuration management and reducing complexity despite the increased number of features.
Solution Approach 2:
The system transforms the complex manual configuration task into an automated process by changing how feature crossing is determined. It uses data-driven methods to automatically identify which features should be crossed among the large number of extracted features, reducing configuration complexity while maintaining the ability to improve model performance through appropriate feature combinations.
3Productivity
If automated feature crossing is implemented, then time consumption and complexity are reduced, but semantic correlation accuracy may decrease
Solution Approach 1:
The system replaces manual semantic analysis with automated computational methods. Instead of relying on human experts to determine semantic correlations, it uses machine learning algorithms and statistical analysis to automatically identify feature correlations, achieving both high efficiency and accurate correlation detection through computational rather than manual means.
Solution Approach 2:
The system changes the method of determining semantic correlation from manual expert judgment to automated data-driven analysis. It uses computational algorithms to calculate feature correlations based on actual data patterns, transforming the measurement approach to achieve both high productivity and maintained accuracy through objective statistical methods rather than subjective manual assessment.
Data Source
AI summary
Embodiments of the present disclosure relate to feature crossing for machine learning. According to example embodiments of the present disclosure, a method comprises determining a semantic correlation relationship between a plurality of feature categories, the semantic correlation relationship indicating respective degrees of semantic correlation between respective pairs of feature categories among the plurality of feature categories; obtaining at least two features classified in at least two of the plurality of feature categories for machine learning; and performing feature crossing on the at least two features based on the semantic correlation relationship.