Patent ID: 11941513
Assignee: KOREA ADVANCED INSTITUTE OF SCIENCE AND TECHNOLOGY
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A device for ensembling data received from a plurality of prediction devices, the device comprising:
a data manager configured to:
provide raw learning data to a first prediction device and a second prediction device,
receive a first device prediction result corresponding to the raw learning data from the first prediction device, and
receive a second device prediction result corresponding to the raw learning data from the second prediction device;

a learner configured to adjust a weight group of a prediction model for generating first item weights depending on first item values of the first and second device prediction results, second item weights depending on second item values of the first and second device prediction results, a first device weight corresponding to the first prediction device, and a second device weight corresponding to the second prediction device, based on the first device prediction result and the second device prediction result; and
a predictor configured to generate an ensemble result from predication results received from the first and second prediction device using the prediction model, wherein the weight group comprises a first parameter for generating the first item weights, a second parameter for generating the second item weights, and a third parameter for generating the first and second device weights,
wherein the learner includes:
an item weight calculator configured to calculate the first item weights, based on the first item values and the first parameter and calculate the second item weights, based on the second item values and the second parameter;
a device weight calculator configured to calculate the first and second device weights, based on the first and second device prediction results and the third parameter;
a weight applicator configured to apply the first and second item weights and the first device weight to the first device prediction result to generate a first result, and apply the first and second item weights and the second device weight to the second device prediction result to generate a second result; and
a weight controller configured to adjust the first parameter, the second parameter, and the third parameter, based on the first and second results, and

wherein the item weight calculator includes a neural network that includes:
a first input layer configured to receive the first item values;
a first embedding layer configured to output a first intermediate result in consideration of a relationship between the first item values; and
a first attention layer configured to analyze the first intermediate result and calculate the first item weights using an attention mechanism.