Methods and systems for training a machine learning system using a reduced data set

A method for training a model using a reduced dataset, the method comprising:

• receiving a first data record comprising a first sequence of characters;
• detecting a first plurality of subsets of the first sequence of characters;
• associating a first subset of the first plurality of subsets with a first symbol, wherein the first subset of the first plurality of subsets corresponds to a first type of character;
• associating a second subset of the first plurality of subsets with a second symbol, wherein the second subset of the first plurality of subsets corresponds to a second type of character;
• determining a first arrangement of the first and second symbols based on an arrangement of the first and second subsets of the first plurality of subsets within the first sequence of characters;
• receiving, from a user input device, user input specifying a first classification for the first symbol and a second classification for the second symbol;
• adding the first data record to a first group of records;
• receiving a second data record comprising a second sequence of characters;
• detecting a second plurality of subsets of the second sequence of characters;
• associating a first subset of the second plurality of subsets with a third symbol, wherein the first subset of the second plurality of subsets corresponds to a third type of character;
• associating a second subset of the second plurality of subsets with a fourth symbol, wherein the second subset of the second plurality of subsets corresponds to a fourth type of character;
• determining a second arrangement of the third and fourth symbols based on an arrangement of the first and second subsets of the second plurality of subsets within the second sequence of characters;
• receiving, from the user input device, user input specifying a third classification for the third symbol and a fourth classification for the fourth symbol;
• determining whether the first arrangement and second arrangement are identical;
• in response to determining that the first arrangement and the second arrangement are identical, adding the second data record to the first group of records;
• in response to determining that the first arrangement and the second arrangement are not identical, adding the second data record to a second group of records;
• feeding the first and second data records to the model as the reduced dataset;
• and
• training the model with both the reduced dataset and the user specified first, second, third and fourth classifications.

A system for training a model using a reduced dataset comprising computer-readable memory, a user input device, and control circuitry configured to:

• receive a first data record comprising a first sequence of characters;
• detect a first plurality of subsets of the first sequence of characters;
• associate a first subset of the first plurality of subsets with a first symbol, wherein the first subset of the first plurality of subsets corresponds to a first type of character;
• associate a second subset of the first plurality of subsets with a second symbol, wherein the second subset of the first plurality of subsets corresponds to a second type of character;
• determine a first arrangement of the first and second symbols based on an arrangement of the first and second subsets of the first plurality of subsets within the first sequence of characters;
• receive, from the user input device, user input specifying a first classification for the first symbol and a second classification for the second symbol;
• add the first data record to a first group of records;
• receive a second data record comprising a second sequence of characters;
• detect a second plurality of subsets of the second sequence of characters;
• associate a first subset of the second plurality of subsets with a third symbol, wherein the first subset of the second plurality of subsets corresponds to a third type of character;
• associate a second subset of the second plurality of subsets with a fourth symbol, wherein the second subset of the second plurality of subsets corresponds to a fourth type of character;
• determine a second arrangement of the third and fourth symbols based on an arrangement of the first and second subsets of the second plurality of subsets within the second sequence of characters;
• receive, from the user input device, user input specifying a third classification for the third symbol and a fourth classification for the fourth symbol;
• determine whether the first arrangement and the second arrangement are identical;
• in response to determining that the first arrangement and the second arrangement are identical, add the second data record to the first group of records;
• in response to determining that the first arrangement and the second arrangement are not identical, add the second data record to a second group of records;
• feed the first and second data records to the model as the reduced dataset;
• and
• train the model with both the reduced dataset and the user specified first, second, third, and fourth classifications.