Cost-sensitive alternating decision trees for record linkage

A non-transitory storage medium arrangement storing computer program instructions that operate on plural records, said storage medium arrangement storing the following computer program instructions:

• (a) blocking instructions that control the computer to block the plural records and store the blocked plural records in a blocked database;
• (b) training instructions that control the computer to train a machine learning model based at least in part on cost-sensitive alternating decision trees using a plurality of training examples in a training database;
• prior to the training, assign the plurality of training examples with a plurality of non-uniformly distributed cost factors for the training to quantify the cost of misclassifying;
• and update a weight distribution in a biased manner towards training examples with higher costs;
• and
• (c) applying instructions that apply a feature set with the generated machine learned model to selectively link the blocked plural records in the blocked database.

A method of operating on plural records comprising:

• (a) with a computing arrangement, blocking the plural records to provide a blocked database;
• (b) training, with the computing arrangement, a plurality of training examples in a training database to generate a machine learned model using a cost-sensitive alternating decision tree, the computing arrangement prior to the training assigning the plurality of training examples with a plurality of non-uniformly distributed costs for the training to quantify the cost of misclassifying, and updating a weight distribution in a biased manner towards training examples with higher costs;
• and
• (c) applying, with the computing arrangement, a feature set with the generated machine learned model to selectively link the blocked plural records in the blocked database.

A system for operating on plural records comprising:

• a computer-based blocker that finds similar records and blocks the similar records to provide a blocked database;
• a computer-based machine learner that uses at least one processor to train on a plurality of training examples in a training database to generate a machine learned model using an alternating decision tree, the computer-based machine learner prior to the training assigning the plurality of training examples with a plurality of non-uniformly distributed costs for the training to quantify the cost of misclassifying, and updating a weight distribution in a biased manner towards training examples with higher costs;
• a computer-based linker that applies a feature set with the generated machine learned model to selectively link the blocked similar records in the blocked database;
• and
• a computer-based execution component connected to the Internet that provides a background check or an identity check on demand at least in part in response to records linked based on said machine-learned model in response to a user search requested over the Internet.