Data Classifier Using Variant Rule Comparison
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Solution Overview
Problem
Existing data processing systems face challenges in accurately classifying ambiguous data, leading to incorrect processing actions, increased network traffic, and resource utilization, especially when dealing with large volumes of unlabeled data across multiple sources.
Innovation Solution
A computer-implemented method and system for data classification that generates variations of a data classification request, applies multiple sets of rules to determine type predictions, and compares these predictions to produce a final classification result, utilizing a processing device and memory with executable instructions to manage data classification requests and batch processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sets of rules are applied to generate type predictions for data classification, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The classification system is segmented into multiple independent rule sets, each responsible for generating type predictions for different aspects of the data. This allows the system to apply specialized classification logic for different data characteristics while maintaining overall system organization and manageability.
Solution Approach 2:
Multiple type predictions from different rule sets are merged through a comparison mechanism that synthesizes the results into a final classification decision. This combining approach leverages the strengths of multiple classification perspectives to achieve higher overall accuracy.
2Productivity
If data classification is performed on ambiguous data across multiple sources, then productivity is improved, but loss of information increases
Solution Approach 1:
The system performs preliminary classification actions by generating multiple type predictions before making a final classification decision. This preliminary multi-perspective analysis helps preserve information about ambiguous data by considering multiple possible interpretations before committing to a final classification.
Solution Approach 2:
The comparison mechanism provides feedback by evaluating multiple type predictions against each other, allowing the system to refine its classification decision based on the consistency and quality of different predictions. This feedback loop helps maintain classification accuracy even when processing ambiguous data at high throughput.
Data Source
AI summary
Examples described herein provide data classification. According to an aspect, a computer-implemented method includes receiving, by a processing device, a data classification request at a data classifier and generating, by the processing device, one or more variations of the data classification request as one or more variants. The computer-implemented method also includes applying, by the processing device, a first set of rules by the data classifier to determine a first type prediction for the one or more variants, and applying, by the processing device, a second set of rules by the data classifier to determine a second type prediction for the one or more variants. The computer-implemented method further includes comparing, by the processing device, the first type prediction with the second type prediction to determine a final type prediction as a data classification result.