Semi-Supervised Data Quality System Using Clustering and Validation
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Solution Overview
Problem
In machine learning, the lack of high-quality labeled data hinders the development of reliable classification-based solutions, as unclean or 'dirty' data can lead to unreliable performance of classifiers, and manual annotation is costly and impractical for large datasets.
Innovation Solution
A system and method for increasing data quality through semi-supervised learning, which uses clustering and classification techniques to assign class labels to unlabeled data, validate predicted labels, and purify dirty data, reducing the need for human intervention by employing algorithms like Manifold-Regularized Support Vector Machines and Laplacian Support Vector Machines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation is used to label data, then data quality and accuracy are improved, but time consumption and cost increase significantly
Solution Approach 1:
The system performs preliminary clustering analysis on unlabeled data to pre-assign cluster labels before final classification. This preliminary action groups similar data points together, reducing the annotation burden and enabling faster processing while maintaining quality through the semi-supervised learning framework.
Solution Approach 2:
The patent introduces clustering algorithms as an intermediary between raw unlabeled data and the final classification model. This intermediary step organizes data into meaningful groups, allowing the system to leverage both labeled and unlabeled data effectively, thereby improving data quality without requiring complete manual annotation of all data points.
2Measurement precision
If complete manual labeling is performed, then data accuracy is improved, but cost and resource requirements become prohibitive
Solution Approach 1:
The system applies partial labeling by focusing manual annotation efforts only on critical or ambiguous data points that fall near cluster boundaries, rather than annotating all data. This selective approach maintains high data accuracy for decision-critical instances while significantly reducing overall annotation costs through the semi-supervised framework.
Solution Approach 2:
The patent uses clustering to create representative copies or prototypes of data groups. By identifying cluster centers and representative samples, the system can generalize from a smaller set of labeled examples to cover entire clusters, reducing the cost of comprehensive labeling while maintaining accuracy through the copying of label patterns across similar data points.
3Productivity
If clustering algorithms are used to assign labels automatically, then processing speed is improved, but data quality may deteriorate without proper validation
Solution Approach 1:
The system implements feedback mechanisms where clustering results are evaluated against labeled data, and the clustering parameters are iteratively adjusted to improve alignment with ground truth. This feedback loop ensures that automatic clustering maintains high data quality by continuously refining its assignments based on performance metrics and validation against known labels.
Solution Approach 2:
The patent employs dynamic clustering approaches where cluster assignments are not fixed but can be adjusted iteratively. The system dynamically refines cluster boundaries and reassigns data points based on emerging patterns and validation results, allowing processing speed to improve while data quality is maintained through adaptive refinement rather than static automatic assignment.
Data Source
AI summary
A method and system for increasing data quality of a dataset for semi-supervised machine learning analysis. The method includes: receiving known class label information for a portion of the data in the dataset; receiving clustering parameters from a user; determining a data cleanliness factor, and where the data cleanliness factor is below a predetermined cleanliness threshold: assigning data without class label information as a data point to a cluster using the clustering parameters, each cluster having a cluster class label associated with such cluster; and determining a measure of assignment, and where the measure of assignment for each data point is below a predetermined assignment threshold, receiving a class label for such data points, otherwise, assigning the respective cluster class label to each data point with the respective measure of assignment below the predetermined assignment threshold; and otherwise, outputting the dataset with associated class labels for machine learning analysis.