System receives inputs, each input associated with a label and having features, creates a rule for each feature, each rule including a feature and a label, each rule stored in a hierarchy, and distributes each rule into a partition associated with a label or another partition associated with another label. System identifies a number of inputs that include a feature for a rule in the rule partition, and identifies another number of inputs that include both the feature for the rule and another feature for another rule in the rule partition. System deletes the rule from the hierarchy if the ratio of the other number of inputs to the number of inputs satisfies a threshold and an additional number of inputs that includes the other antecedent feature is at least as much as the number. System predicts a label for an input including features by applying each remaining rule to the input.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system for rule set induction, the system comprising: one or more processors; and a non-transitory computer readable medium storing a plurality of instructions, which when executed, cause the one or more processors to: create a corresponding rule for each feature in a training set, the training set comprising a plurality of inputs, each of the plurality of inputs being associated with a corresponding output label and comprising a corresponding set of features, each rule being stored in a rule set hierarchy and comprising a corresponding antecedent feature and a corresponding consequent label; distribute each rule into one of a partition of rules associated with a consequent label and another partition of rules associated with another consequent label; identify a number of inputs that comprise an antecedent feature of a rule in the partition of rules; identify another number of inputs that comprise both the antecedent feature of the rule in the partition of rules and another antecedent feature of another rule in the partition of rules; determine whether a ratio of the other number of inputs to the number of inputs satisfies a threshold and an additional number of inputs that includes the other antecedent feature is at least as much as the number; delete the rule from the rule set hierarchy in response to a determination that the ratio of the other number of inputs to the number of inputs satisfies the threshold and the additional number of inputs that includes the other antecedent feature is at least as much as the number; and predict a corresponding output label for an input comprising a corresponding set of features by applying each remaining rule in the rule set hierarchy to the input. 2. The system of claim 1, wherein the corresponding output label comprises a categorical value of a plurality of categorical values. 3. The system of claim 1, wherein the corresponding consequent label is associated with the corresponding output label for a percentage of corresponding features in the training set, the percentage of corresponding features satisfying a confidence level. 4. The system of claim 1, wherein the plurality of instructions to create a corresponding rule for each feature in the training set further comprises instructions, which when executed, cause the one or more processors to: create a generalized feature by generalizing a feature that is associated with a number of inputs; determine whether the generalized feature is associated with more than the number of inputs; and create a corresponding rule for the generalized feature in response to a determination that the generalized feature is associated with more than the number of inputs. 5. The system of claim 4, wherein generalizing the feature comprises replacing any suffix numerals with corresponding suffix placeholders, and replacing any suffix placeholders with a single suffix placeholder. 6. The system of claim 1, comprising further instructions, which when executed, cause the one or more processors to: predict, by the system, an output label for an input in the training set by applying each remaining rule in the rule set hierarchy to the training set; and create, by the system, an additional rule to predict a correct output label for the input in the training set in response to a determination that the output label is incorrect, the additional rule superseding other rules in the rule set hierarchy that apply to the input in the training set. 7. The system of claim 6, wherein the plurality of instructions to create the additional rule to predict the correct output label for the input in the training set further comprises instructions, which when executed, cause the one or more processors to: create an additional rule to predict the correct output label for a first number of inputs in the training set that comprise the input in the training set; create another additional rule to predict the correct output label for a second number of inputs in the training set that comprise the input in the training set; determine whether the first number of inputs in the training set is at least as many as the second number of inputs in the training set, and remove the other additional rule from the rule set hierarchy in response to a determination that the first number of inputs in the training set is at least as many as the second number of inputs in the training set. 8. A computer program product comprising computer-readable program code to be executed by one or more processors when retrieved from a non-transitory computer-readable medium, the program code including instructions to: create a corresponding rule for each feature in a training set, the training set comprising a plurality of inputs, each of the plurality of inputs being associated with a corresponding output label and comprising a corresponding set of features, each rule being stored in a rule set hierarchy and comprising a corresponding antecedent feature and a corresponding consequent label; distribute each rule into one of a partition of rules associated with a consequent label and another partition of rules associated with another consequent label; identify a number of inputs that comprise an antecedent feature of a rule in the partition of rules; identify another number of inputs that comprise both the antecedent feature of the rule in the partition of rules and another antecedent feature of another rule in the partition of rules; determine whether a ratio of the other number of inputs to the number of inputs satisfies a threshold and an additional number of inputs that includes the other antecedent feature is at least as much as the number; delete the rule from the rule set hierarchy in response to a determination that the ratio of the other number of inputs to the number of inputs satisfies the threshold and the additional number of inputs that includes the other antecedent feature is at least as much as the number; and predict a corresponding output label for an input comprising a corresponding set of features by applying each remaining rule in the rule set hierarchy to the input. 9. The computer program product of claim 8, wherein the corresponding output label comprises a categorical value of a plurality of categorical values. 10. The computer program product of claim 8, wherein the corresponding consequent label is associated with the corresponding output label for a percentage of corresponding features in the training set, the percentage of corresponding features satisfying a confidence level. 11. The computer program product of claim 8, wherein the program code instructions to create a corresponding rule for each feature in the training set further comprise instructions, which when executed, cause the one or more processors to: create a generalized feature by generalizing a feature that is associated with a number of inputs; determine whether the generalized feature is associated with more than the number of inputs; and create a corresponding rule for the generalized feature in response to a determination that the generalized feature is associated with more than the number of inputs. 12. The computer program product of claim 11, wherein generalizing the feature comprises replacing any suffix numerals with corresponding suffix placeholders, and replacing any suffix placeholders with a single suffix placeholder. 13. The computer program product of claim 8, wherein the program code comprises further instructions to: predict, by the system, an output label for an input in the training set by applying each remaining rule in the rule set hierarchy to the training set; and create, by the system, an additional rule to predict a correct output label for the input in the training set in response to a determination that the output label is incorrect, the additional rule superseding other rules in the rule set hierarchy that apply to the input in the training set. 14. The computer program product of claim 13, wherein the program code instructions to create the additional rule to predict the correct output label for the input in the training set further comprise instructions, which when executed, cause the one or more processors to: create an additional rule to predict the correct output label for a first number of inputs in the training set that comprise the input in the training set; create another additional rule to predict the correct output label for a second number of inputs in the training set that comprise the input in the training set; determine whether the first number of inputs in the training set is at least as many as the second number of inputs in the training set, and remove the other additional rule from the rule set hierarchy in response to a determination that the first number of inputs in the training set is at least as many as the second number of inputs in the training set. 15. A method comprising: creating, by a processor of a computing system, a corresponding rule for each feature in a training set, the training set comprising a plurality of inputs, each of the plurality of inputs being associated with a corresponding output label and comprising a corresponding set of features, each rule being stored in a rule set hierarchy and comprising a corresponding antecedent feature and a corresponding consequent label; distributing, by the processor of the computing system, each rule into one of a partition of rules associated with a consequent label and another partition of rules associated with another consequent label; identifying, by the processor of the computing system, a number of inputs that comprise an antecedent feature of a rule in the partition of rules; identifying, by the processor of the computing system, another number of inputs that comprise both the antecedent feature of the rule in the partition of rules and another antecedent feature of another rule in the partition of rules; determining, by the processor of the computing system, whether a ratio of the other number of inputs to the number of inputs satisfies a threshold and an additional number of inputs that includes the other antecedent feature is at least as much as the number; deleting, by the processor of the computing system, the rule from the rule set hierarchy in response to a determination that the ratio of the other number of inputs to the number of inputs satisfies the threshold and the additional number of inputs that includes the other antecedent feature is at least as much as the number; and predicting, by the processor of the computing system, a corresponding output label for an input comprising a corresponding set of features by applying each remaining rule in the rule set hierarchy to the input. 16. The method of claim 15, wherein the corresponding output label comprises a categorical value of a plurality of categorical values, the corresponding consequent label is associated with the corresponding output label for a percentage of corresponding features in the training set, and the percentage of corresponding features satisfying a confidence level. 17. The method of claim 15, wherein creating a corresponding rule for each feature in the training set comprises: creating, by the processor of the computing system, a generalized feature by generalizing a feature that is associated with a number of inputs; determining, by the processor of the computing system, whether the generalized feature is associated with more than the number of inputs; and creating, by the processor of the computing system, a corresponding rule for the generalized feature in response to a determination that the generalized feature is associated with more than the number of inputs. 18. The method of claim 17, wherein generalizing the feature comprises replacing any suffix numerals with corresponding suffix placeholders, and replacing any suffix placeholders with a single suffix placeholder. 19. The method of claim 15, wherein the method further comprises: predicting, by the processor of the computing system, an output label for an input in the training set by applying each remaining rule in the rule set hierarchy to the training set; and creating, by the processor of the computing system, an additional rule to predict a correct output label for the input in the training set in response to a determination that the output label is incorrect, the additional rule superseding other rules in the rule set hierarchy that apply to the input in the training set. 20. The method of claim 19, wherein creating the additional rule to predict the correct output label for the input in the training set comprises: creating, by the processor of the computing system, an additional rule to predict the correct output label for a first number of inputs in the training set that comprise the input in the training set; creating, by the processor of the computing system, another additional rule to predict the correct output label for a second number of inputs in the training set that comprise the input in the training set; determining, by the processor of the computing system, whether the first number of inputs in the training set is at least as many as the second number of inputs in the training set, and removing, by the processor of the computing system, the other additional rule from the rule set hierarchy in response to a determination that the first number of inputs in the training set is at least as many as the second number of inputs in the training set.