Machine Learning Data Classification with Confidence Scores
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Solution Overview
Problem
Manual data classification is labor-intensive, costly, and prone to errors, requiring subject matter experts to categorize large volumes of data assets, making it impractical for large-scale data governance.
Innovation Solution
A machine-learning based methodology that uses classification machines with machine learning models to generate confidence scores for data assets, presented via an interface with user-selectable options to accept or override classifications, facilitating human-machine collaboration for efficient data classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual classification is used, then classification accuracy can be maintained through expert inspection, but labor intensity and cost increase significantly
Solution Approach 1:
The patent replaces manual mechanical classification processes with automated machine learning systems. ML models analyze data assets and generate classification predictions, substituting human expert inspection with algorithmic processing. This maintains classification accuracy through trained models while dramatically improving productivity by automating the classification workflow.
Solution Approach 2:
The system enables self-service classification where the machine learning models autonomously perform classification tasks without requiring continuous human intervention. The models process data assets, generate predictions, and provide confidence scores automatically, allowing the system to serve itself in the classification process while humans only need to review and validate results.
2Reliability
If manual classification is used, then expert judgment ensures reliable categorization, but time consumption increases for large volumes of data
Solution Approach 1:
The system performs preliminary classification actions through machine learning models before human review. ML models pre-process data assets, generate classification predictions, and calculate confidence scores in advance. This preliminary action filters and prepares data for human validation, significantly reducing the time experts need to spend while maintaining reliability through the combination of automated prediction and human oversight.
Solution Approach 2:
The system implements feedback loops where classification results are continuously refined. Human reviewers validate ML predictions and provide corrections, which are then fed back to retrain and improve the models. This feedback mechanism ensures classification reliability improves over time while maintaining efficient processing speeds through automated initial classification.
3Productivity
If automated machine learning classification is implemented, then processing speed increases, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary layer between raw data and final classification decisions. Machine learning models act as intermediaries that process data assets and generate predictions with confidence scores. This intermediary layer simplifies the overall system architecture by centralizing the classification logic in trained models, making the system more manageable despite the increased automation capability.
Solution Approach 2:
The classification system is segmented into multiple independent machine learning models, each trained for specific classification tasks. This segmentation allows the complex classification problem to be divided into manageable components, where each model handles a specific aspect of data asset classification. The modular architecture improves productivity while making the overall system complexity more controllable through independent model development and deployment.
4Productivity
If machine learning models are used for classification, then labor costs decrease, but implementation and maintenance costs increase
Solution Approach 1:
The system leverages parameter changes in machine learning models to achieve cost efficiency. By training models on historical data and adjusting parameters through continuous learning, the system improves classification performance over time. This allows the organization to reduce labor costs associated with manual classification while the ML models adapt to new data patterns, offsetting implementation costs through improved automation efficiency.
Data Source
AI summary
Methods, apparatus, systems, computing devices, computing entities, and/or the like for employing machine learning concepts to accurately predict categories for unseen data assets, present the same to a user via a user interface for review, and assign the categories to the data assets responsive to user interaction confirming the same.