A predictive analytics system and method in the setting of multi-class classification are disclosed, for identifying systematic changes in an evaluation dataset processed by a fraud-detection model by examining the time series histories of an ensemble of entities such as accounts. The ensemble of entities is examined and processed both individually and in aggregate, via a set of features determined previously using a distinct training dataset. The specific set of features in question may be calculated from the entity's time series history, and may or may not be used by the model to perform the classification. Certain properties of the detected changes are measured and used to improve the efficacy of the predictive model.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method comprising: selecting, by at least one data processor, at least one attribute of one or more input data records; partitioning, by the at least one data processor, one or more datasets according to a partitioning scheme that is based on the at least one attribute, each of the one or more datasets having at least one input data record; generating, by the at least one data processor, a set of features based on historical data records, each of the historical data records having a known class membership, the set of features being generated according to different class labels for the known class membership; calculating, by the at least one data processor, a set of microscopic derived features for each new input data record and/or previous input data records, based on the partitioning and the set of features, the set of microscopic derived features being related to the selected at least one attribute; generating, by the at least one data processor, a set of macroscopic derived features based on the aggregated set of microscopic derived features calculated for the new and previous input data records based on the partitioning and the set of features, the set of macroscopic derived features representing concept drift of the one or more datasets; generating, by the at least one data processor, a first probability score representing a likelihood of an extent and magnitude of the concept drift; generating, by the at least one data processor using the set of microscopic derived features and the set of macroscopic derived features for previous input data records, a second probability score representing a likelihood that a new input data record is subject to the extent and magnitude of the concept drift representing by the first probability score; and adjusting, by the at least one data processor, a class membership for the new input data record based on the second probability score and according to the magnitude of the concept drift. 2. The method in accordance with claim 1, wherein microscopic derived features are associated with entity defined by the partitioning scheme. 3. The method in accordance with claim 1, wherein the class membership adjustment is trained according to the partitioning scheme. 4. The method in accordance with claim 1, wherein the class membership adjustment is trained according to the historical data records. 5. The method in accordance with claim 1, wherein the class membership adjustment includes a binary classification for fraud detection. 6. The method in accordance with claim 1, wherein the at least one processor executes a feed-forward neural network for adjusting the class membership, the set of features based on the historical data records being hidden nodes of the feed-forward neural network. 7. The method in accordance with claim 1, wherein partitioning the one or more data records includes generating a summary of the historical data records prior to generating the set of features based on the historical data records. 8. A computer program product comprising a non-transitory machine-readable medium storing instructions that, when executed by at least one programmable processor, cause the at least one programmable processor to perform operations comprising: selecting at least one attribute of one or more input data records; partitioning one or more datasets according to a partitioning scheme that is based on the at least one attribute, each of the one or more datasets having at least one input data record; generating a set of features based on historical data records, each of the historical data records having a known class membership, the set of features being generated according to different class labels for the known class membership; calculating a set of microscopic derived features for each new input data record and/or previous input data records, based on the partitioning and the set of features, the set of microscopic derived features being related to the selected at least one attribute; generating a set of macroscopic derived features based on the aggregated set of microscopic derived features calculated for the new and previous input data records based on the partitioning and the set of features, the set of macroscopic derived features representing concept drift of the one or more datasets; generating a first probability score representing a likelihood of an extent and magnitude of the concept drift; generating, using the set of microscopic derived features and the set of macroscopic derived features for previous input data records, a second probability score representing a likelihood that a new input data record is subject to the extent and magnitude of the concept drift representing by the first probability score; and adjusting a class membership for the new input data record based on the second probability score and according to the magnitude of the concept drift. 9. The computer program product in accordance with claim 8, wherein microscopic derived features are associated with entity defined by the partitioning scheme. 10. The computer program product in accordance with claim 8, wherein the class membership adjustment is trained according to the partitioning scheme. 11. The computer program product in accordance with claim 8, wherein the class membership adjustment is trained according to the historical data records. 12. The computer program product in accordance with claim 8, wherein the class membership adjustment includes a binary classification for fraud detection. 13. The computer program product in accordance with claim 8, wherein the at least one processor executes a feed-forward neural network for adjusting the class membership, the set of features based on the historical data records being hidden nodes of the feed-forward neural network. 14. The computer program product in accordance with claim 8, wherein partitioning the one or more data records includes generating a summary of the historical data records prior to generating the set of features based on the historical data records. 15. A system comprising: at least one programmable processor; and a non-transitory, machine-readable medium storing instructions that, when executed by the at least one processor, cause the at least one programmable processor to perform operations comprising: select at least one attribute of one or more input data records; partition one or more datasets according to a partitioning scheme that is based on the at least one attribute, each of the one or more datasets having at least one input data record; generate a set of features based on historical data records, each of the historical data records having a known class membership, the set of features being generated according to different class labels for the known class membership; calculate a set of microscopic derived features for each new input data record and/or previous input data records, based on the partitioning and the set of features, the set of microscopic derived features being related to the selected at least one attribute; generate a set of macroscopic derived features based on the aggregated set of microscopic derived features calculated for the new and previous input data records based on the partitioning and the set of features, the set of macroscopic derived features representing concept drift of the one or more datasets; generate a first probability score representing a likelihood of an extent and magnitude of the concept drift; generate, using the set of microscopic derived features and the set of macroscopic derived features for previous input data records, a second probability score representing a likelihood that a new input data record is subject to the extent and magnitude of the concept drift representing by the first probability score; and adjust a class membership for the new input data record based on the second probability score and according to the magnitude of the concept drift. 16. The system in accordance with claim 15, wherein microscopic derived features are associated with entity defined by the partitioning scheme. 17. The system in accordance with claim 15, wherein the class membership adjustment is trained according to the partitioning scheme. 18. The system in accordance with claim 15, wherein the class membership adjustment is trained according to the historical data records. 19. The system in accordance with claim 15, wherein the class membership adjustment includes a binary classification for fraud detection. 20. The system in accordance with claim 15, wherein the at least one processor executes a feed-forward neural network for adjusting the class membership, the set of features based on the historical data records being hidden nodes of the feed-forward neural network. 21. The system in accordance with claim 15, wherein partitioning the one or more data records includes generating a summary of the historical data records prior to generating the set of features based on the historical data records.