AI Feature Engineering System Integrating Domain Knowledge
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Solution Overview
Problem
Current feature engineering in machine learning is primitive, relying on brute-force application of candidate transformation methods, and lacks integration of human domain knowledge with artificial intelligence, limiting the creation of complex and valuable features for predictive models.
Innovation Solution
A system that integrates human feature engineering with AI by identifying domains in datasets, archiving domain knowledge features, and allowing users to define and select features collaboratively, generating predictive models based on selected features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If brute-force transformation methods are applied to existing features, then feature generation is automated, but feature quality and domain knowledge integration deteriorate
Solution Approach 1:
The system introduces an intermediary component - a domain knowledge database - that bridges automated transformation methods and high-quality feature generation. This database stores pre-validated transformation rules and domain-specific knowledge that guide the automated feature engineering process, ensuring that generated features maintain high quality while still achieving automation.
Solution Approach 2:
The system performs preliminary action by pre-processing and storing domain knowledge and transformation rules in a database before the actual feature engineering process. This preparation work includes curating domain-specific features, validation rules, and transformation methodologies that will be reused during automated feature generation, thereby improving both quality and efficiency.
2Manufacturing precision
If human experts manually craft features, then feature quality and domain knowledge integration improve, but productivity and time consumption deteriorate
Solution Approach 1:
The system applies copying by capturing and storing expert-crafted features and domain knowledge in a reusable database. Once human experts create high-quality features through manual processes, these features and the knowledge behind them are copied into the system's knowledge base, allowing the same quality to be replicated automatically without requiring continuous human intervention for each new feature engineering task.
Solution Approach 2:
The system enables self-service by allowing the automated feature engineering process to independently generate features using stored domain knowledge and transformation rules. The system serves itself by automatically selecting appropriate transformations, generating candidate features, and even performing initial validation without requiring continuous human expert involvement, thereby dramatically improving productivity while maintaining quality.
3Adaptability or versatility
If comprehensive candidate transformation methods are applied, then feature coverage increases, but system complexity and computational cost increase
Solution Approach 1:
The system segments the comprehensive set of transformation methods into organized categories stored in the domain knowledge database. Instead of applying all possible transformations uniformly, the system divides transformations into domain-specific groups (e.g., numerical transformations, categorical transformations, time-series transformations) and selects only the relevant segments for each feature engineering task, thereby maintaining versatility while reducing complexity.
Solution Approach 2:
The system applies partial action by selectively applying only the necessary subset of transformation methods rather than exhaustively applying all possible transformations. The domain knowledge database enables the system to identify and apply only the relevant transformations for each specific feature and domain context, achieving sufficient feature coverage without the computational overhead of exhaustive transformation application.
Data Source
AI summary
A domain of an input dataset is identified and one or more archived domain knowledge features corresponding to the identified domain are identified. One or more user feature definitions for one or more user features defined by a user are inputted. The identified archived domain knowledge features and the user features are processed to generate a set of candidate features for presentation to the user. A selection of a subset of the candidate features is obtained from the user and one or more predictive models are generated based on the selected features.