US 12,437,209 B2
Method for real-time enhancement of a predictive algorithm by a novel measurement of concept drift using algorithmically-generated features
Scott Michael Zoldi, San Diego, CA (US); Jim Coggeshall, San Diego, CA (US); and Yuting Jia, San Diego, CA (US)
Assigned to Fair Isaac Corporation, Minneapolis, MN (US)
Filed by FAIR ISAAC CORPORATION, Roseville, MN (US)
Filed on Sep. 9, 2021, as Appl. No. 17/470,951.
Application 17/470,951 is a continuation of application No. 14/880,130, filed on Oct. 9, 2015, granted, now 11,144,834.
Prior Publication US 2021/0406703 A1, Dec. 30, 2021
This patent is subject to a terminal disclaimer.
Int. Cl. G06N 20/00 (2019.01); G06F 17/18 (2006.01); G06N 3/084 (2023.01); G06N 5/02 (2023.01); G06N 7/01 (2023.01)
CPC G06N 5/02 (2013.01) [G06F 17/18 (2013.01); G06N 3/084 (2013.01); G06N 7/01 (2023.01); G06N 20/00 (2019.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method embedded in a non-transitory machine-readable medium, the method executable by one or more processors running on a machine configured for classification of events, the method comprising:
partitioning at least one dataset having at least one input data record based on an attribute associated with one or more input data records fed to a predictive model that classifies one or more events;
generating a set of features based on one or more historical data records fed to the predictive model according to different class labels for known class memberships associated with the one or more historical data records;
determining a first set of features for the at least one input data record based on the generated set of features and the partitioning of the at least one dataset;
generating a second set of features based on the first set of features, the second set of features representing a concept drift associated with the at least one dataset, wherein the concept drift represents a systematic shift in statistical distribution properties of new data records input over time to train the predictive model, the concept drift resulting in less accurate event classification by the predictive model, and wherein the new data records and the one or more historical data records are mutually exclusive;
responsive to at least one of the first set of features and the second set of features, generating a first score and a second score,
the first score representing a likelihood of an extent or magnitude of the concept drift, and
the second score representing a likelihood that the at least one new input data record is subject to the extent or magnitude of the concept drift as represented by the first score; and
adjusting a class membership for the new input data record based on the second score and according to the extent or magnitude of the concept drift to improve accurate classification in the predictive model.