Geodata Match Ensemble Using Weak Supervision
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Solution Overview
Problem
Machine learning classifiers trained on geodata are prone to errors due to data variability, leading to inaccurate results in matching location names with the same or similar geolocation.
Innovation Solution
An ensemble system is developed, combining a location name classifier and a geo-similarity classifier, using weak supervision-based labeling functions and geo-coordinate transformations to generate accurate match classifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single machine learning classifier is used to match location names, then the system is simple to implement, but the accuracy is low and prone to errors due to data variability
Solution Approach 1:
The patent combines multiple classifiers (location name classifier and geo-similarity classifier) into an ensemble system that aggregates their match classifications. This merging approach improves overall accuracy by leveraging the strengths of different classification methods while mitigating individual weaknesses caused by data variability.
Solution Approach 2:
The patent introduces an ensemble aggregation mechanism as an intermediary that processes outputs from multiple classifiers. This mediator combines match classifications from different sources, resolving conflicts and producing a final, more accurate match determination that overcomes limitations of individual classifiers.
2Adaptability or versatility
If weak supervision-based labeling is used to handle data variability, then the system can process unstructured content, but false positives and false negatives increase
Solution Approach 1:
The patent merges the location name classifier (which handles unstructured text data with weak supervision labeling) with the geo-similarity classifier (which uses structured geographic coordinates). This combination maintains adaptability to unstructured data while improving reliability by cross-validating results against geographic proximity information.
Solution Approach 2:
The ensemble system implements feedback mechanisms where match classifications from one classifier inform and adjust the processing of the other classifier. This feedback loop helps identify and correct false positives and false negatives by comparing consistent patterns across multiple classification approaches.
3Reliability
If multiple classifiers are ensembled to improve accuracy, then match classification reliability increases, but computational complexity and processing time increase
Solution Approach 1:
The patent segments the classification process into two independent but complementary classifiers that can operate in parallel. The location name classifier processes text-based similarity while the geo-similarity classifier processes coordinate-based proximity, allowing concurrent execution that reduces overall processing time compared to sequential approaches.
Solution Approach 2:
The ensemble system applies partial action by using confidence thresholds and voting mechanisms to determine when additional classifier processing is necessary. For high-confidence matches from either classifier, the system can terminate early without requiring full ensemble processing, reducing average processing time while maintaining reliability.
Data Source
AI summary
The disclosure relates to methods and systems of generating geodata match classifications based on an ensemble of weak supervision-based match labels and geo-similarity models. The system may generate at least two match classifiers that are ensembled together. The match classifiers may include a location name classifier and a geo-similarity classifier. The location name classifier may generate a match classification that is based on a similarity of characters of the location names. The geo-similarity classifier may generate a match classification that is based on a proximity of the geolocations defined by the location names. The match classifications may be aggregated together to generate a geodata match classification.