Systems and methods for machine learning feature processing are disclosed. An example method includes: selecting a predefined number of machine-based transactions; identifying a first set of features based on the predefined number of machine-based transactions; and applying a machine learning feature selection algorithm to identify a second set of features. The second set of features is smaller than the first set of features, and the second set of features includes no more than a predefined number of features. The method also includes constructing a decision tree based on the second set of features, wherein the decision tree has a depth that is less than a predefined depth. The method optionally includes limiting the total number of features in the second set to no greater than the predefined number of features.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method, comprising: determining a limit on a total number of transaction categorization rules to be generated; selecting a predefined number of machine-based transactions; determining a first set of features based on the predefined number of machine-based transactions; applying one or more machine learning feature selection algorithms to determine a second set of features, wherein the second set of features is smaller in number than the first set of features, and the second set of features includes no more than a predefined number of features; constructing a decision tree based on the second set of features, wherein the decision tree has a depth that is less than a predefined depth; and the decision tree includes a plurality of nodes corresponding to the second set of features; and applying a test data set to the decision tree to determine an accuracy level for each node included in the decision tree; selecting, from the plurality of nodes, a subset of nodes that are associated with a predefined accuracy level; determining a third set of features associated with the subset of nodes; and generating a set of human-readable transaction categorization rules based on the third set of features, wherein a total number of human-readable transaction categorization rules included in the set is fewer than the limit. 2. The method of claim 1, further comprising: selecting a pending transaction; and categorizing the pending transaction based on the set of human-readable transaction categorization rules. 3. The method of claim 1, wherein applying the one or more machine learning feature selection algorithms comprises: applying a plurality of iterations of a machine learning feature selection algorithm; or applying a plurality of machine learning feature selection algorithms. 4. The method of claim 1, wherein determining the second set of features comprises: determining a first feature and a second feature; determining that an association of both the first feature and the second feature provides a higher level of transaction accuracy than does the first feature or the second feature alone; and responsive to the determining, including the first feature and the second feature in the second set of features. 5. The method of claim 1, further comprising: determining a transaction maturation time; and selecting the limit based on the transaction maturation time. 6. The method of claim 1, wherein the second set of features includes one of a total transaction amount of a first party included in a machine-based transaction, a recent activity level of the first party, a transaction amount of the machine-based transaction, or a transaction location of the machine-based transaction. 7. The method of claim 1, wherein the one or more machine learning feature selection algorithms includes one of: a Markov blanket algorithm, a CorrSF algorithm, a Fast Correlation Based Filter (FCBF) algorithm, or a RELIEFF algorithm. 8. The method of claim 1, wherein the machine-based transactions include matured transactions that have completed and been classified as a benign transaction or a fraudulent transaction. 9. The method of claim 1, wherein the machine-based transactions include transactions conducted between one or more user devices and one or more merchant devices. 10. A non-transitory machine-readable medium having stored thereon machine-readable instructions executable to cause a machine to perform operations comprising: determining a limit on a total number of transaction categorization rules to be generated; selecting a predefined number of machine-based transactions; determining a first set of features based on the predefined number of machine-based transactions; applying one or more machine learning feature selection algorithms to determine a second set of features, wherein the second set of features is smaller in number than the first set of features, and the second set of features includes no more than a predefined number of features; constructing a decision tree based on the second set of features, wherein the decision tree has a depth that is less than a predefined depth; and the decision tree includes a plurality of nodes corresponding to the second set of features; and applying a test data set to the decision tree to determine an accuracy level for each node included in the decision tree; selecting, from the plurality of nodes, a subset of nodes that are associated with a predefined accuracy level; determining a third set of features associated with the subset of nodes; and generating a set of human-readable transaction categorization rules based on the third set of features, wherein a total number of human-readable transaction categorization rules included in the set is fewer than the limit. 11. The non-transitory machine-readable medium of claim 10, wherein determining the second set of features comprises: determining a first feature and a second feature; determining that an association of both the first feature and the second feature provides a higher level of transaction accuracy than does the first feature or the second feature alone; and responsive to the determining, including the first feature and the second feature in the second set of features. 12. The non-transitory machine-readable medium of claim 10, wherein the machine-based transactions include matured transactions that have been classified as a benign transaction or a fraudulent transaction. 13. The non-transitory machine-readable medium of claim 10, wherein generating the set of human-readable transaction categorization rules based on the third set of features includes: generating a description corresponding to a human-readable transaction categorization rule based on a property and a value associated with a feature. 14. The non-transitory machine-readable medium of claim 10, wherein applying the one or more machine learning feature selection algorithms comprises: applying a plurality of iterations of a machine learning feature selection algorithm; or applying a plurality of machine learning feature selection algorithms. 15. The non-transitory machine-readable medium of claim 10, wherein the operations further comprise: determining a transaction maturation time; and selecting the limit based on the transaction maturation time. 16. A system, comprising: a non-transitory memory; and one or more hardware processors coupled to the non-transitory memory and configured to execute instructions to perform operations comprising: determining a limit on a total number of transaction categorization rules to be generated; selecting a predefined number of machine-based transactions; determining a first set of features based on the predefined number of machine-based transactions; applying one or more machine learning feature selection algorithms to identify a second set of features, wherein the second set of features is smaller in number than the first set of features, and the second set of features includes no more than a predefined number of features; constructing a decision tree based on the second set of features, wherein the decision tree has a depth that is less than a predefined depth; and the decision tree includes a plurality of nodes corresponding to the second set of features; and applying a test data set to the decision tree to determine an accuracy level for each node included in the decision tree; selecting, from the plurality of nodes, a subset of nodes that are associated with a predefined accuracy level; determining a third set of features associated with the subset of nodes; and generating a set of human-readable transaction categorization rules based on the third set of features, wherein a total number of human-readable transaction categorization rules included in the set is fewer than the limit. 17. The system of 16, wherein determining the second set of features comprises: determining a first feature and a second feature; determining that association of both the first feature and the second feature provides a higher level of transaction accuracy than does the first feature or the second feature alone; and responsive to the determining, including the first feature and the second feature in the second set of features. 18. The system of 16, wherein the machine-based transactions include matured transactions that have been classified as a benign transaction or a fraudulent transaction. 19. The system of 16, wherein generating the set of human-readable transaction categorization rules based on the third set of features includes: generating a description corresponding to a human-readable transaction categorization rule based on a property and a value associated with a feature. 20. The system of 16, wherein applying the one or more machine learning feature selection algorithms comprises: applying a plurality of iterations of a machine learning feature selection algorithm; or applying a plurality of machine learning feature selection algorithms.