Patent ID: 11861510
Assignee: NASDAQ, INC.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 11:
12. A method performed on a computer system that includes at least one hardware processor, the method comprising:
storing a plurality of datasets, with each one of the plurality of datasets including a corresponding plurality of features;
storing a plurality of weights, wherein each of the plurality of features of each of the plurality of datasets is assigned one of the plurality of weights;
(a) generating a feature set by selecting multiple features from among datasets of the plurality of datasets, wherein at least one feature from each of the datasets is selected to be included in the multiple features, wherein each one of the multiple features is selected based on the weight that is assigned to that corresponding one of the multiple features;
(b) performing a training process using data values for each of the multiple features of the generated feature set to generate a model;
(c) calculating metagradient data for the feature set based on the model that has been generated, wherein the calculated metagradient data represents how the generated feature set performed for an objective function;
(d) adjusting, by using the calculated metagradient data for the feature set, at least one of the weight(s) that is assigned to a corresponding one of the multiple features of the generated feature set; and
repeating at least (a)-(d) for a plurality of iterations, with each iteration generating a new feature set according to (a) that takes into account how the at least one of the weight(s) have been adjusted according to (d), wherein (a)-(d) are repeated until a convergence criteria is satisfied for the feature set that has been generated from (a) for the model that is generated from (b), wherein the objective function is used in each of the plurality of iterations, wherein composition of the generated feature set changes across the plurality of iterations that are repeated.