Real-Time Feature Similarity Detection via Vector Embeddings
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Solution Overview
Problem
Current machine learning (ML) feature engineering lacks optimization for feature comparison and similarity detection, leading to duplicate features that waste resources and increase development time and costs due to the lack of efficient systems for identifying and utilizing existing similar features.
Innovation Solution
Implement a system that uses feature attribute clustering and similarity distances to detect similar features in real-time, employing an ML clustering engine to compute similarity scores and generate cluster representatives, allowing for the identification and reuse of existing features during feature engineering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If feature engineering is performed without similarity detection, then feature creation flexibility is maintained, but duplicate features are created wasting computing resources and storage
Solution Approach 1:
The system performs preliminary clustering of features into groups based on similarity metrics before actual feature engineering tasks. This pre-organization allows rapid identification of duplicate or similar features during feature creation, preventing wasteful computation without requiring complex real-time comparison of all features.
Solution Approach 2:
The patent introduces feature embeddings as an intermediary representation that transforms discrete feature definitions into continuous vector spaces. These embeddings serve as mediators that enable efficient similarity computation through mathematical operations (cosine similarity, Euclidean distance) rather than complex feature-by-feature comparison, reducing computational complexity.
2Loss of time
If feature similarity detection is implemented, then duplicate feature creation is reduced, but processing time for feature engineering increases
Solution Approach 1:
The system pre-computes feature embeddings and clusters features into groups before feature engineering tasks begin. This preliminary processing organizes features in a way that enables O(1) or O(log n) lookup time during feature creation, rather than requiring O(n) comparison time, thus reducing overall feature engineering time despite the initial processing overhead.
Solution Approach 2:
The patent creates compressed representations (embeddings) of features that capture essential characteristics in reduced dimensional spaces. These embeddings serve as efficient copies that enable rapid similarity assessment without requiring access to or processing of the full original feature definitions, significantly speeding up comparison operations.
3Ease of manufacture
If features are created without optimization, then feature creation simplicity is maintained, but resource utilization efficiency decreases
Solution Approach 1:
The system automatically performs feature embedding generation, similarity computation, and duplicate detection without requiring manual intervention from data scientists. The feature engineering platform self-services by integrating these optimization functions directly into the feature creation workflow, maintaining simplicity for users while achieving resource efficiency through automated analysis and suggestions.
Data Source
AI summary
There are provided systems and methods for detection of similar machine learning features in real-time for declarative feature engineering. A service provider, such as an electronic transaction processor for digital transactions, may utilize computing services that implement machine learning models for decision-making of data including real-time data in production computing environments. Machine learning models may utilize features or variables that may correspond to coded logic that provides a measurable datum, property, or the like to the models for intelligent outputs. When creating features, preexisting features may accomplish the same or similar function. Thus, the service provider may provide machine learning clustering of features for similarity detection in real-time. Feature clusters may be precomputed and loaded for comparison using representative vectors for clusters. Each feature may have a declarative definition of parameters that may be used for comparison, and similar detected features may be output during feature engineering.