Autonomous Feature Vector Generation via Reinforcement Learning
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Solution Overview
Problem
Existing feature selection processes for machine learning algorithms are cumbersome and inefficient, requiring manual intervention and trial-and-error, as they struggle to automate the selection of optimal features from large datasets, leading to high computational costs and reduced decision-making speed.
Innovation Solution
A system that generates a fixed-size composite feature vector using a taxonomic graph and an autonomous deep reinforcement learning agent, which iteratively determines features that maximize long-term rewards, thereby automating the feature engineering process and optimizing feature selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual feature selection is performed by data scientists using trial and error, then feature quality may be improved through expert intuition, but time consumption and computational costs increase significantly
Solution Approach 1:
The system enables automated feature engineering where the algorithm selects and composes features autonomously without requiring manual intervention. The self-service framework allows the system to automatically train models, evaluate features, and refine feature sets through iterative composition, eliminating the need for data scientists to perform manual trial-and-error feature selection while maintaining high feature quality through automated validation mechanisms.
Solution Approach 2:
The patent replaces the mechanical manual process of feature selection with an automated computational system. Instead of data scientists manually creating and testing features, the system uses automated feature composition techniques that programmatically generate, evaluate, and refine feature sets, substituting human manual labor with algorithmic processes that can systematically explore feature spaces and identify optimal features at scale.
2Reliability
If the number of possible features is increased to improve model accuracy, then predictive performance may be enhanced, but computational complexity and validation costs become intractable
Solution Approach 1:
The system segments the overwhelming feature space into manageable components by organizing features hierarchically and processing them in structured stages. Instead of evaluating all possible features simultaneously, the system divides feature composition into sequential steps where features are built incrementally from base features through controlled transformations, making the complex search space tractable while still achieving high model accuracy through systematic exploration.
Solution Approach 2:
The system employs partial action by evaluating and selecting only the most promising subset of features rather than exhaustively testing all possible feature combinations. Through automated validation and performance monitoring, the system identifies and focuses on high-value features while avoiding unnecessary computation on low-impact features, achieving strong model accuracy with a manageable feature set that avoids intractable complexity.
3Productivity
If automated feature selection algorithms are implemented to reduce manual effort, then productivity may be improved, but the ability to capture domain expertise and nuanced feature relationships deteriorates
Solution Approach 1:
The system incorporates feedback mechanisms where automated feature selection algorithms continuously monitor model performance and use validation results to guide subsequent feature composition decisions. This feedback loop allows the system to learn from performance outcomes and adjust feature selection strategies accordingly, capturing effective feature patterns automatically while maintaining the ability to incorporate domain knowledge through performance-based validation and iterative refinement that reflects real-world data characteristics.
Data Source
AI summary
Systems and methods include determination of a plurality of features, determination, for each of the plurality of features, of a feature vector based on a taxonomy of logical entities, combination of the determined feature vectors into a composite feature vector, determination of an operator based on the composite feature vector, and determination of a new feature based on the operator.