Storage medium, estimation method, and information processing device, relearning program, and relearning method

An estimation method for a computer of retraining a machine learning model using, as supervised learning data, data with labels each of which is targeted for estimation and assigned by a clustering technique, the estimation method comprising:

• specifying, for each training cluster of a plurality of training clusters, a representative point from among a plurality of points included in the each training cluster to obtain a plurality of representative points for the plurality of training clusters, each of the plurality of training clusters identifiably corresponding to one of the labels targeted for estimation, the plurality of training clusters being generated by clustering, in a feature space, a training data set used in a training phase for training of the machine learning model that estimates the labels according to input data in the training data set, wherein a number of the plurality of training clusters is equal to a number of the labels targeted for estimation;
• setting boundaries between each of a plurality of input clusters under a condition that a number of the plurality of input clusters and the number of the representative points coincide with each other, the plurality of input clusters being generated by clustering, in the feature space, an input data set used in an operation phase of the trained machine learning model;
• acquiring estimation results for the labels with respect to the input data based on a correspondence relationship between the plurality of input clusters and the plurality of training clusters after the setting of the boundaries;
• and
• re-training the machine learning model using, as the supervised learning data, a combination of the input data set and labels derived from the estimation results.

An estimation device of retraining a machine learning model using, as supervised learning data, data with labels each of which is targeted for estimation and assigned by a clustering technique, the estimation device comprising:

• one or more memories;
• and
• one or more processors coupled to the one or more memories and the one or more processors configured to:
• specify, for each training cluster of a plurality of training clusters, a representative point from among a plurality of points included in the each training cluster to obtain a plurality of representative points for the plurality of training clusters, each of the plurality of training clusters identifiably corresponding to one of the labels targeted for estimation, the plurality of training clusters being generated by clustering, in a feature space, a training data set used in a training phase for training of the machine learning model that estimates the labels according to input data in the training data set, wherein a number of the plurality of training clusters is equal to a number of the labels targeted for estimation,
• set boundaries between each of a plurality of input clusters under a condition that a number of the plurality of input clusters and the number of the representative points coincide with each other, the plurality of input clusters being generated by clustering, in the feature space, an input data set used in an operation phase of the trained machine learning model,
• acquire estimation results for the labels with respect to the input data set based on a correspondence relationship between the plurality of input clusters and the plurality of training clusters after the setting of the boundaries, and
• re-training the machine learning model using, as the supervised learning data, a combination of the input data set and labels derived from the estimation results.