Method for building classifier models for event classes via phased rule induction
First Claim
1. A program storage device readable by machine, tangibly embodying a program of instructions executable by the machine to perform method steps for predicting a target class within a dataset, the method steps comprising:
- determining positive rules predicting the presence of a plurality of examples of the target class;
determining negative rules predicting the absence of the target class among the plurality of examples of the target class predicted to be present; and
applying a classifier model to the dataset for determining the presence the target class according to the positive rules and negative rules.
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Abstract
A method for learning signatures of a target class using a sequential covering phased rule-induction. The method balances recall and precision for the target class. A first phase aims for high recall by inducing rules with high support and a reasonable level of accuracy. A second phase improves the precision by learning rules to remove false positives in the collection of the records covered by the first phase rules, while keeping the overall recall at a desirable level. The method constructs a mechanism to assign prediction probability scores to each classification decision. The model includes a set of positive rules that predict presence of the target class, a set of negative rules that predict absence of the target class, and a set of prediction score values corresponding to each pair-wise combination of positive and negative rules. The two-phase method is extensible to a multiphase approach.
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Citations
20 Claims
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1. A program storage device readable by machine, tangibly embodying a program of instructions executable by the machine to perform method steps for predicting a target class within a dataset, the method steps comprising:
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determining positive rules predicting the presence of a plurality of examples of the target class;
determining negative rules predicting the absence of the target class among the plurality of examples of the target class predicted to be present; and
applying a classifier model to the dataset for determining the presence the target class according to the positive rules and negative rules. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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6. The program storage device of claim 5, wherein the recall is a predefined portion of the examples in the target class.
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7. The program storage device of claim 5, wherein the precision is a predefined fraction of correctly predicted target class examples among all predicted examples.
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8. The program storage device of claim 1, wherein the presence prediction achieves at least a predefined recall and the absence prediction achieves at least a predefined precision.
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9. The program storage device of claim 8, wherein achieving the desired precision includes the steps of:
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collecting the examples predicted by the presence prediction; and
predicting a false positive example among the examples predicted by the presence prediction.
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10. A method for learning a classifier model which determines examples of a target class in a dataset comprising the steps of:
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learning a plurality of positive rules supporting a plurality of examples of the target class;
learning a plurality of negative rules removing a plurality of false positive examples among the examples supported by the positive rules;
weighing an effect of each negative rule on each positive rule; and
applying the classifier model to the dataset for determining the presence of the target class according to a plurality of positive rules, the plurality of negative rules, and the effect of each negative rule on each positive rule. - View Dependent Claims (11, 12, 13, 14, 15)
determining a contribution of each negative rule; and
comparing each contribution to a predefined description length, upon determining that the contribution is greater than the description length, ending the learning of negative rules.
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14. The method of claim 10, wherein a weight of each effect corresponds to a probability of a given supported example belonging to the target class.
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15. The method of claim 10, wherein a negative rule/positive rule combination having a low weight is ignored by the classifier model.
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16. A method for learning a classifier model which predicts the presence of a target class in a dataset comprising the steps of:
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learning a plurality of P-Rules supporting a plurality of examples of the target class;
learning a plurality of N-Rules removing a plurality of false positive examples among the examples supported by the P-Rules;
assigning a probabilistic score to each N-Rule/P-Rule combination; and
applying the classifier model to the dataset for determining the presence of the target class according to a plurality of positive rules, the plurality of negative rules, and the probabilistic scores, wherein each probabilistic score is compared to a threshold to recover at least one example of the target class removed by the plurality of N-Rules. - View Dependent Claims (17, 18, 19, 20)
learning a plurality of P-Rules individual supporting at least a first predefined number of examples and collectively supporting at least a second predefined number of examples;
learning a plurality of P-Rules having at least a predefined accuracy upon determining that the second predefined number of examples are supported; and
learning the N-Rules upon determining that a subsequent P-Rule has an accuracy less than a predefined accuracy.
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18. The method of claim 16, wherein the step of learning the N-Rules further comprises the steps of:
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determining a cost for an N-Rule; and
comparing the cost to a predefined description length, upon determining that the cost is greater than the description length, ending the learning of N-Rules.
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19. The method of claim 16, wherein the step of learning the P-Rules is via sequential covering.
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20. The method of claim 16, wherein the step of learning the N-Rules is via sequential covering.
Specification