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

Claim 0:
1. An information processing apparatus configured to automatically conduct machine learning operations to improve a plurality of machine learning models by analyzing and replacing machine learning models in the plurality of machine learning models and extract an optimized model of the machine learning operations with a lower load at a higher speed, comprising:
at least one memory configured to store program code; and
electronic circuitry including at least one of an Application Specific Integrated Circuit (ASIC), Field Programmable Gate Array (FGPA), and processor, the processor being configured to operate as instructed by the program code, the electronic circuitry configured to:
calculate 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;
select, as parent models, a plurality of models from the population having higher value of the fitness calculated among the plurality of models in the population;
generate 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;
store 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;
calculate the fitness of the virtual model using the predetermined function;
perform a replacing operation to update the population by adding the virtual model to the population and by deleting a model among the plurality of models in the population having a lowest value of the fitness from the population;
repeat selecting of the parent models from the updated population, generating of the virtual model, storing of the information in association with the virtual model, calculating the fitness of the virtual model, and performing the replacing operation until a predetermined termination condition is reached; and
automatically extract, 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.