Abstract: The disclosure relates to methods and systems of match classification based on ensemble modeling. Match classification based on ensemble modeling is a prediction that two or more data records match or mismatch one another based on the output of two or more classifiers trained to make this prediction. A first match classifier may include an ensemble of decision trees, which may be trained via gradient boosting, and in particular using extreme gradient boosting to generate a first match classification. The first match classifier, which is itself an ensembled model, may be ensembled together with at least a second match classifier trained via weak supervision to generate a second match classification, which may be aggregated with the first match classification. The aggregated classification may be used to determine whether or not two or more match candidates match one another.

Abstract: 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.

Abstract: 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.

Abstract: The disclosure relates to methods and systems of match classification based on ensemble modeling. Match classification based on ensemble modeling is a prediction that two or more data records match or mismatch one another based on the output of two or more classifiers trained to make this prediction. A first match classifier may include an ensemble of decision trees, which may be trained via gradient boosting, and in particular using extreme gradient boosting to generate a first match classification. The first match classifier, which is itself an ensembled model, may be ensembled together with at least a second match classifier trained via weak supervision to generate a second match classification, which may be aggregated with the first match classification. The aggregated classification may be used to determine whether or not two or more match candidates match one another.