公开(公告)号：US11295242B2

公开(公告)日：2022-04-05

申请号：US16682946

申请日：2019-11-13

申请人： International Business Machines Corporation

发明人： Yuan-Chi Chang ,  Deepak Srinivas Turaga ,  Long Vu ,  Venkata Nagaraju Pavuluri ,  Saket Sathe ,  Rodrigue Ngueyep Tzoumpe

IPC分类号： G06N20/10 ,  G06F17/18 ,  G06K9/62 ,  G06N3/02 ,  G06N3/08 ,  G06N3/04 ,  G06N5/00 ,  G06N20/20

摘要： Split an input dataset into training and test datasets; the former includes a plurality of data examples, each represented as a feature vector, and having an associated true label. Split the training dataset into a plurality of training data subsets; for each, train a corresponding machine learning model to obtain a plurality of such models, and apply same to the test dataset to obtain a plurality of predicted labels and prediction scores. For each of the plurality of examples, compute an agreement metric based on a corresponding one of the associated true labels; corresponding ones of the predicted labels; and corresponding ones of the prediction scores. Based on the computed metric, select, for at least some of the true label values, appropriate ones of the data examples to be added to a regression set. Add the appropriate ones of the data examples from the test dataset to the regression set.

公开(公告)号：US20210142222A1

公开(公告)日：2021-05-13

申请号：US16682946

申请日：2019-11-13

申请人： International Business Machines Corporation

发明人： Yuan-Chi Chang ,  Deepak Srinivas Turaga ,  Long Vu ,  Venkata Nagaraju Pavuluri ,  Saket Sathe ,  Rodrigue Ngueyep Tzoumpe

IPC分类号： G06N20/10 ,  G06N3/02 ,  G06K9/62 ,  G06F17/18

摘要： Split an input dataset into training and test datasets; the former includes a plurality of data examples, each represented as a feature vector, and having an associated true label. Split the training dataset into a plurality of training data subsets; for each, train a corresponding machine learning model to obtain a plurality of such models, and apply same to the test dataset to obtain a plurality of predicted labels and prediction scores. For each of the plurality of examples, compute an agreement metric based on a corresponding one of the associated true labels; corresponding ones of the predicted labels; and corresponding ones of the prediction scores. Based on the computed metric, select, for at least some of the true label values, appropriate ones of the data examples to be added to a regression set. Add the appropriate ones of the data examples from the test dataset to the regression set.