Model learning apparatus, model learning method, and program

A computer-implemented method for model learning for three-valued classification, the method comprising:

• generating normal data for learning positive results;
• generating abnormal data for learning negative results;
• generating a learning data set based on the normal data and the abnormal data;
• learning a classification model using the generated learning data set based on a predetermined area under-the-receiver-operating-characteristic curve (AUC) values, wherein the AUC values are based at least on a difference between a first abnormality degree of the normal data and a second abnormality degree of the abnormal data using a two-stage step operation.

A system for a three-valued classification, the system comprising:

• a processor;
• and
• a memory storing computer-executable instructions that when executed by the processor cause the system to:
• generate normal data for learning positive results;
• generate abnormal data for learning negative results;
• generate a learning data set based on the normal data and the abnormal data;
• learn a classification model using the generated learning data set based on a predetermined area under-the-receiver-operating-characteristic curve (AUC) values, wherein the AUC values are based at least on a difference between a first abnormality degree of the normal data and a second abnormality degree of the abnormal data using a two-stage step operation.

A computer-readable non-transitory recording medium storing computer-executable instructions that when executed by a processor cause a computer system to:

• generate normal data for learning positive results;
• generate abnormal data for learning negative results;
• generate a learning data set based on the normal data and the abnormal data;
• learn a classification model using the generated learning data set based on a predetermined area under-the-receiver-operating-characteristic curve (AUC) values, wherein the AUC values are based at least on a difference between a first abnormality degree of the normal data and a second abnormality degree of the abnormal data using a two-stage step operation.