Automated Dataset Generation for Machine Learning Models
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Solution Overview
Problem
Organizations face challenges in transitioning from rule-based systems to machine learning solutions due to the need for large datasets, data structure changes, lack of clear decision-making transparency, and potential biases in machine learning models, which can lead to inefficiencies and unusable models during re-training.
Innovation Solution
An automated dataset generation system that uses a rules set and data model to generate labeled and unlabeled datasets, allowing for the training, validation, and testing of machine learning models without requiring extensive field data collection, thereby addressing data scarcity and bias issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are trained on historical data, then the models can learn from real-world patterns, but the models inherit biases present in the historical data and lack transparency in decision-making
Solution Approach 1:
The system performs preliminary actions by generating synthetic training data that embeds desired ethical constraints and transparent decision logic before the actual model training occurs. This prevents bias inheritance by establishing clean, controllable training data upfront rather than attempting to correct biases after they are learned from historical data.
Solution Approach 2:
Synthetic data acts as an intermediary between the training process and real-world applications. This intermediary layer allows the system to decouple model training from biased historical data while still producing models that perform well on real-world tasks, thereby reducing bias inheritance and improving transparency.
2Adaptability or versatility
If data structures change after machine learning model training, then the system can adapt to new requirements, but the model becomes unusable and requires re-training which takes significant time
Solution Approach 1:
The system generates diverse synthetic data covering multiple data structure variations in advance. This preliminary preparation ensures that when data structures change, pre-generated synthetic data matching the new structures is already available, enabling rapid model re-training without time-consuming data collection.
Solution Approach 2:
The system serves itself by automatically generating the synthetic training data it needs when data structures change. This self-service capability eliminates the need for manual data collection and preparation, allowing rapid adaptation to new data structures without external intervention or significant time investment.
3Quantity of substance
If organizations collect sufficient field data for machine learning training, then robust models can be trained, but the process is time-consuming and resource-intensive
Solution Approach 1:
The system creates copies of real-world data scenarios through synthetic data generation. Instead of collecting actual field data, the system generates synthetic copies that replicate the statistical properties, patterns, and complexity of real data, providing sufficient training data volume without the time and resources required for field data collection.
Solution Approach 2:
The system changes the parameters of data generation by using configurable rules and constraints to produce synthetic data with specific distributions, relationships, and characteristics. This allows flexible generation of diverse training data volumes by adjusting generation parameters rather than investing proportional time in field data collection.
Data Source
AI summary
A computer-implemented method includes detecting attributes and values in rules contained in a rules set. Definitions of the attributes are determined from a data model associated with the rules set. Multiple different data entries having fields corresponding to the attributes are generated by populating the fields with data according to the values detected in the rules and the definitions of the attributes determined from the data model. A labeled dataset is formed using the data entries and logic contained in the rules. At least a portion of the labeled dataset is used to train a machine learning.