Data Classification Using Probability-Based Ambiguity Resolution
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Solution Overview
Problem
Existing data classification methods face challenges in efficiently handling large datasets with ambiguities due to multiple valid data class candidates, often requiring user intervention and expertise, which can lead to incorrect classifications and complicate data processing.
Innovation Solution
A method that uses a classifier to determine confidence values for data fields, identifies data class candidates, and employs probabilities based on previous user-selected assignments and metadata to accurately classify data fields, reducing the need for user intervention and ensuring accurate classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional data classification methods are used, then user intervention and expertise are required to handle ambiguities, but this leads to increased complexity, time consumption, and potential for incorrect classifications
Solution Approach 1:
The system automatically resolves classification ambiguities by computing probabilities from multiple data class candidates and selecting the most likely class without requiring user intervention. The classifier independently handles uncertain cases by evaluating confidence values and making automated decisions, eliminating the need for manual expert review while maintaining high classification accuracy.
Solution Approach 2:
The system changes the parameter of decision-making from manual user judgment to automated probability-based selection. By computing probability scores for each data class candidate and comparing them against thresholds, the system transforms the classification process from a complex human-in-the-loop operation to a straightforward automated parameter comparison, reducing system complexity while improving reliability.
2Reliability
If manual user intervention is used to resolve classification ambiguities, then expertise can be applied, but this increases time consumption and reduces productivity
Solution Approach 1:
The system performs self-service by automatically resolving classification ambiguities through probability computation and automated decision-making. Instead of requiring manual user intervention for each ambiguous case, the classifier independently evaluates confidence values, computes probabilities for multiple candidates, and selects the most appropriate data class, thereby maintaining high accuracy while dramatically increasing processing speed and productivity.
Solution Approach 2:
The system performs preliminary action by pre-computing probability scores for all data class candidates before final classification decisions are made. This preliminary probability assessment allows the system to quickly resolve ambiguities during the classification process without requiring time-consuming manual review, thereby maintaining accuracy while improving overall processing efficiency.
3Reliability
If multiple data class candidates are considered for ambiguous fields, then classification accuracy can be improved, but this increases the complexity of the classification process
Solution Approach 1:
The system transforms the complex multi-candidate classification problem into a simple parameter comparison task. By computing probability scores for each candidate and comparing them against each other and against thresholds, the system reduces the decision-making process to a straightforward parameter evaluation, making the classification process easy to operate while maintaining high accuracy through consideration of multiple candidates.
Solution Approach 2:
The system introduces probability computation as an intermediary mechanism between multiple data class candidates and the final classification decision. This intermediary layer evaluates and ranks all candidates based on their likelihood, providing a clear basis for selection and simplifying the overall classification process while ensuring accurate results through systematic evaluation of all possibilities.
4Productivity
If automated classification is used without user intervention, then productivity increases, but the system may make incorrect classifications in ambiguous cases
Solution Approach 1:
The system uses parameter changes by computing and comparing probability scores for multiple data class candidates. Instead of making simple automated decisions based on single criteria, the system evaluates confidence values and probability distributions, selecting the class with the highest probability. This parameter-based approach enables automated processing at high speed while maintaining high accuracy by making informed decisions based on quantitative evidence.
Solution Approach 2:
The system implements feedback by using probability computations to guide automated classification decisions. The classifier evaluates confidence values for each candidate, uses these probabilities to select the most likely class, and can adjust its decision-making based on the distribution of probabilities. This feedback mechanism ensures that automated classification maintains high accuracy by continuously evaluating the quality of its own predictions.
Data Source
AI summary
A method provides for classifying data fields of a dataset. A classifier configured for determining confidence values for a plurality of data classes for the data fields may be applied. Using the confidence values, data class candidates may be identified. Data fields may be determined for which a plurality of data class candidates is identifiable. Using previous user-selected data class assignments, a probability may be determined for the data class candidates that the respective data class candidate is a data class to which the respective data field is to be assigned. The data fields may be classified using the probabilities to select for the data fields a data class from the data class candidates. The dataset may be provided with metadata identifying for the data fields the data classes to which the respective data fields are assigned.