Automated Dataset Preparation for Machine Learning
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Solution Overview
Problem
The current process of preparing datasets for machine-learning models is manual, time-consuming, and expensive, often resulting in truncated datasets that can lead to inaccurate or incomplete analysis due to the need for manual flattening, parsing, and optimization, which may overlook relevant patterns.
Innovation Solution
A method and system that automatically calculates pattern relevance, modifies dataset focus, adds contextual information, and parses fields into numerical form, optimizing datasets for machine-learning model ingestion and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual dataset preparation is performed, then data quality can be controlled, but time consumption and cost increase significantly
Solution Approach 1:
The system performs automated pattern relevance calculation, contextual cue detection, and numerical scheme identification without requiring manual intervention. The machine-learning model itself guides the preparation process by indicating what patterns it needs, making the system self-sufficient in data preparation tasks.
Solution Approach 2:
Manual mechanical processes of data preparation (flattening, parsing, optimization) are replaced with automated computational processes. The system uses algorithmic approaches to detect contextual cues and transform data formats, substituting human labor with machine-based automated systems.
2Ease of manufacture
If manual flattening and parsing is performed, then data format requirements are met, but relevant patterns may be overlooked
Solution Approach 1:
The system uses pattern relevance calculations to provide feedback on which data fields and contextual cues are most valuable for the machine-learning model. This feedback mechanism ensures that during flattening and parsing, the most relevant patterns are preserved and prioritized, preventing information loss.
Solution Approach 2:
The system performs preliminary detection of contextual cues and calculation of pattern relevance before the actual flattening and parsing operations. This preliminary analysis ensures that the subsequent data transformation processes are guided by knowledge of which patterns are important, preventing oversight of relevant information.
3Measurement precision
If comprehensive dataset preparation is performed, then analysis accuracy improves, but processing cost increases
Solution Approach 1:
The system dynamically adjusts preparation parameters such as the depth of contextual cue detection and the granularity of pattern relevance calculation based on the specific requirements of the machine-learning model. This parameter optimization ensures comprehensive preparation where needed while reducing unnecessary processing elsewhere, balancing accuracy with cost.
4Productivity
If automated preparation is implemented, then time and cost are reduced, but complexity of the system increases
Solution Approach 1:
The system is designed to perform multiple functions within a unified automated framework: pattern relevance calculation, contextual cue detection, numerical scheme identification, and data transformation. This multi-functionality consolidates what would otherwise require separate complex systems into a single integrated solution, managing complexity while maintaining high productivity.
Data Source
AI summary
A method of preparing a dataset may comprise calculating a pattern relevance for a first field in the dataset and modifying the first field based on the pattern relevance. The method may further comprise detecting a contextual cue in the first field. The method may further comprise retrieving contextual information for a value in the first field and adding that contextual information to the database. Finally, the method may further comprise identifying a numerical scheme for the first field and parsing the first field into a number according to that numerical scheme.