| CPC G16H 50/70 (2018.01) [G16H 30/40 (2018.01)] | 8 Claims |
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1. A mining method for sample grouping, comprising:
performing following steps through a processor:
(a) obtaining an existing model that has been trained and a field dataset that is different from a training data set of the existing model, wherein the field dataset comprises a plurality of samples collected based on a specified field, and the plurality of samples comprises a plurality of corresponding actual labeled results, and a sample number of the field dataset is smaller than a sample number of the training data set of the existing model;
(b) inputting the plurality of samples respectively into the existing model, so as to obtain a plurality of estimated results;
(c) removing an outlier sample set from the field dataset based on a difference distribution of the plurality of estimated results and the plurality of actual labeled results, wherein the plurality of samples that remain in the field dataset after the outlier sample set is removed form a remaining sample set;
(d) grouping the remaining sample set into a hard sample set and an easy sample set based on the plurality of estimated results of the remaining sample set; and
(e) after obtaining the hard sample set and the easy sample set, sending the hard sample set and the easy sample set into an incremental learning framework to retrain the existing model, so that a retrained existing model becomes a model directed to the specified field corresponding to the field dataset to interpret data obtained in the specified field.
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