Calculating an aggregate of attribute values associated with plural cases

• US 20080103849A1
• Filed: 10/31/2006
• Published: 05/01/2008
• Est. Priority Date: 10/31/2006
• Status: Abandoned Application

• Current Assignee: Micro Focus LLC (Open Text Corporation)
• Original Assignee: Micro Focus LLC (Open Text Corporation)
• Inventors: Kirshenbaum, Evan R., Forman, George H.
• Application Number:
  US11/590,466
• Publication Number:
  US 20080103849A1
• Time in Patent Office: Days
• US Class Current: 705/7
• CPC Class Codes: G06F 18/217 Validation; Performance evaluation; Active pattern learning techniques
• CPC Class Codes: G06F 18/24 Classification techniques
• CPC Class Codes: G06Q 10/06375 Prediction of business process outcome or impact based on a proposed change
• CPC Class Codes: G06Q 30/0278 Product appraisal

Abstract

To calculate an aggregate of attribute values associated with plural cases, at least one parameter setting that affects a number of cases predicted positive by a classifier is selected. At least one measure pertaining to the plural cases is calculated, where the at least one measure is dependent upon the selected at least one parameter setting. An estimated quantity of the plural cases relating to at least one class is received. The aggregate of attribute values associated with the plural cases is calculated based on the estimated quantity and the at least one measure

Claims

1. A method comprising:

selecting at least one parameter setting that affects a number of cases predicted positive by a classifier;

determining at least one measure pertaining to plural cases, the at least one measure dependent upon the selected at least one parameter setting;

receiving an estimated quantity of the plural cases relating to at least one class; and

calculating an aggregate of attribute values associated with the plural cases based on the estimated quantity and the at least one measure.

2. The method of claim 1, wherein selecting the at least one parameter setting comprises selecting one of:

a parameter setting that is more conservative than a natural parameter setting of the classifier; and

a parameter setting that is less conservative than the natural parameter setting of the classifier.

3. The method of claim 1, wherein selecting the at least one parameter setting comprises selecting plural parameter settings, and wherein determining the at least one measure comprises determining plural measures corresponding to the plural parameter settings, the method further comprising:

determining a value that is calculated from the plural measures,

wherein calculating the aggregate of attribute values is based on the determined value.

4. The method of claim 3, wherein determining the value comprises one of:

selecting a median measure from among the plural measures;

calculating an arithmetic mean of the plural measures;

calculating a geometric mean of the plural measures;

calculating a mode based on the plural measures;

calculating an ordinal value of the plural measures; and

calculating a value based on a distribution parameter associated with the plural measures.

5. The method of claim 3, further comprising excluding at least one of the plural measures when determining the value.

6. The method of claim 3, wherein determining the value that is calculated from the plural measures is based on a regression technique.

7. The method of claim 1, wherein selecting the at least one parameter setting comprises selecting a less conservative parameter setting, the method further comprising performing an adjustment of the at least one measure to account for reduced precision of the classifier due to selection of the less conservative parameter setting.

8. The method of claim 7, wherein determining the at least one measure comprises computing a first measure, a second measure, and a precision measure, wherein the precision measure represents a precision of the classifier, the first measure is based on cases having scores produced by the classifier having a predefined relationship with respect to the selected parameter setting, and the second measure is computed based on the first measure and the precision measure,

wherein calculating the aggregate of attribute values is based on the second measure.

9. The method of claim 1, wherein determining the at least one measure comprises determining an average cost of cases predicted positive by the classifier, and wherein calculating the aggregate of the attribute values comprises calculating a total cost associated with all the plural cases.

10. A method comprising:

determining a first value of a particular attribute for cases identified as positives for an issue by a classifier;

determining a second value of the particular attribute for cases identified as positives for the issue by the classifier;

computing weights to apply to the first and second values; and

calculating an aggregate of attribute values associated with plural cases based on the weights and the first and second values.

11. The method of claim 10, wherein determining the first value comprises computing a first cost for the identified as positive cases, and determining the second value comprises computing a second cost for the identified as negative cases.

12. The method of claim 11, wherein computing the first cost comprises computing a first total cost for the positive cases, and computing the second cost comprises computing a second total cost for the negative cases.

13. The method of claim 10, wherein computing the weights comprises computing a first weight to apply to the first value and a second weight to apply to the second value, and wherein computing the first weight comprises computing the first weight based on one of a false positive rate and true positive rate of the classifier, and computing the second weight comprises computing the second weight based on a false negative rate of the classifier.

14. The method of claim 10, further comprising:

calculating, for the cases, corresponding uncertainty values representing uncertainties of labeling respective cases,

wherein computing the weights is based on the uncertainty values.

15. The method of claim 14, wherein computing the weights is further based on at least some of a false positive rate of the classifier, a false negative rate of the classifier, and a false negative rate of the classifier.

16. The method of claim 15, wherein calculating the uncertainty values for corresponding cases comprises based on one of:

(1) scores produced by the classifier for the cases;

(2) distances between the scores and a classification threshold of the classifier;

(3) a data structure mapping uncertainty values to scores produced by classifiers applied to training cases;

(4) data associated with the cases;

(5) scores produced by another classifier; and

(6) decisions about cases by a combination of classifiers.

17. Instructions on a computer-usable medium that when executed cause a computer to:

determine at least one parameter that is indicative of a performance of a classifier;

determine at least one measure pertaining to plural cases, the at least one measure dependent upon the at least one parameter that is indicative of the performance of the classifier;

receive an estimated quantity of the plural cases relating to at least one class, wherein the estimated quantity is different from a quantity of cases identified by a classifier as relating to the at least one class; and

calculate an aggregate of attribute values associated with the plural cases based on the estimated quantity and the at least one measure.

18. The instructions of claim 17, wherein determining the at least one parameter comprises one of:

(1) selecting at least one classification threshold of the classifier; and

(2) determining at least some of a false positive rate, a false negative rate, and true positive rate, and

wherein determining the at least one measure comprises determining at least one of;

(1) an attribute value to be multiplied with the estimated quantity to derive the aggregate; and

(2) weights to be applied to corresponding attribute values for producing the aggregate.

19. The instructions of claim 17, wherein determining the at least one measure is based on attribute values associated with the cases, wherein at least one of the cases is missing the attribute value, the instructions when executed causing the computer to handle the missing attribute value by one of (1) ignoring the case with the missing attribute value;

and (2) predicting the missing attribute value from one or more other attributes associated with the case with the missing attribute value.

20. The instructions of claim 17, wherein determining the at least one measure is based on values of an attribute associated with the cases, and wherein the instructions when executed cause the computer to not apply the attribute as a feature for the classifier.

21. A method comprising:

computing a precision measure that indicates a precision of a classifier;

determining at least one measure pertaining to plural cases;

adjusting the at least one measure based on the precision measure; and

calculating an aggregate of attribute values associated with the plural cases based on an estimated quantity and the adjusted at least one measure.

22. The method of claim 21, further comprising selecting at least one parameter setting that affects the number of cases predicted positive by the classifier.