Patent ID: 11928698
Assignee: RAKUTEN GROUP, INC.
Field: IT methods for management (Electrical engineering)
Classification: CPC G | IPC G

Claim 14:
15. A non-transitory computer readable medium having a computer program stored thereon, the computer program configured to cause a computer to automatically conduct machine learning operations to analyze and improve machine learning models, and extract an optimized model of the machine learning operations with a lower load at a higher speed, comprising:
a first fitness calculating process for calculating a fitness using a predetermined function for each of a plurality of machine learning models belonging to a population, the fitness being based on a utility value quantitatively indicating a model utility by substituting at least one model metric as a parameter into a utility function;
a virtual model generating process for:
selecting, as parent models, a plurality of models from the population having higher value of the fitness calculated by the first fitness calculating process from the plurality of models; and

generating a virtual model that outputs an output result obtained by performing calculation of output results of the selected plurality of parent models and has information indicating the selected plurality of parent models that output the output results serving as a basis of the calculation, wherein the virtual model does not have an entity as a learning model and includes information on an output result of each of the plurality of parent models and a generation of each of the plurality of parent models;
a storing process for storing the information of the output results of the plurality of parent models in a memory in association with the generated virtual model and the information of the generation of the plurality of parent models in association with the virtual model;
a second fitness calculating process for calculating the fitness of the virtual model using the predetermined function;
a replacing process for updating the population that adds the virtual model to the population and deletes a model among the plurality of models in the population having a lowest value of the fitness from the population;
a repeating process for repeating the virtual module generating process, the storing process, the second fitness calculating process, and the replacing process until a predetermined termination condition is reached; and
a model extracting process for automatically extracting, a model having a higher value of the fitness from the updated population when the predetermined termination condition is reached, thereby extracting the optimized model of the machine learning operations for the predetermined function with a lower load at a higher speed by using the virtual model that does not have an entity as a learning model.