CLASSIFICATION USING FEATURE SCALING

• US 20080101689A1
• Filed: 10/25/2006
• Published: 05/01/2008
• Est. Priority Date: 10/25/2006
• Status: Active Grant

• Current Assignee: Micro Focus LLC (Open Text Corporation)
• Original Assignee: Hewlett-Packard Development Company, L.P. (HP Inc.)
• Inventors: Forman, George Henry
• Granted Patent: US 8,885,928 B2
• Time in Patent Office: Days
• US Class Current: 382/159
• CPC Class Codes: G06F 18/2113 by ranking or filtering the set of features, e.g. using a measure of variance or of feature cross-correlation

Abstract

Provided are systems, methods and techniques for machine-learning classification. In one representative embodiment, an item having values for a plurality of different features in a feature set is obtained, together with scores for the different features. The score for a given feature is a measure of prediction ability for that feature and was calculated as a function of a plurality of different occurrence metrics of the feature. The values for the features are scaled according to the scores for the features, and the item is classified by inputting the adjusted feature set values for the item into a previously trained classifier.

Claims

1. A method of machine-learning classification, comprising:

(a) obtaining an item having values for a plurality of different features in a feature set;

(b) obtaining scores for the different features, the score for a given feature being a measure of prediction ability for the given feature and having been calculated as a function of a plurality of different occurrence metrics pertaining to the given feature;

(c) scaling the values for the features according to the scores for said features, thereby obtaining adjusted feature set values for the item; and

(d) classifying the item by inputting the adjusted feature set values for the item into a previously trained classifier.

2. A method according to claim 1, wherein the given score is a measure of separation between the plurality of occurrence metrics, relative to an assumed probability distribution.

3. A method according to claim 1, wherein the plurality of occurrence metrics include a true positive rate for the given feature and a false positive rate for the given feature.

4. A method according to claim 3, wherein the score for the given feature is calculated as S=|aF⁻

1(tpr)−

bF<sup>−

1</sup>(fpr)|, where S is the score, tpr is the true positive rate of the given feature, fpr is the false positive rate of the given feature, |*| is an absolute value, F<sup>−

1</sup>(*) is an inverse of an assumed probability distribution function, and a and b are constants.

5. A method according to claim 3, wherein the score for the given feature is a measure of separation between the true positive rate and the false positive rate.

6. A method according to claim 5, wherein the measure of separation between the true positive rate and the false positive rate is calculated relative to an assumed probability distribution.

7. A method according to claim 6, wherein the assumed probability distribution comprises a normal cumulative probability distribution function.

8. A method according to claim 1, wherein the values for the features are scaled such that ranges of values for the different features are proportionate to the scores corresponding to said different features.

9. A method according to claim 1, further comprising a step of eliminating at least one feature from the feature set prior to classifying the item.

10. A method of machine learning classification, comprising:

(a) obtaining an item having values for a plurality of different features in a feature set;

(b) obtaining scores for the different features, the score for a given feature being a measure of the prediction ability of said given feature;

(c) scaling the values for the features according to the scores for said features, thereby obtaining adjusted feature set values for the item; and

(d) classifying the item by inputting the adjusted feature set values for the item into a supervised machine-learning classifier.

11. A method according to claim 10, wherein the score for the given feature was calculated as a function of a plurality of different occurrence metrics pertaining to the given feature.

12. A method according to claim 11, wherein the occurrence metrics include a count of proper classification based on the given feature and a count of improper classification based on the given feature.

13. A method according to claim 11, wherein the given score is a measure of separation between the plurality of occurrence metrics, relative to an assumed probability distribution.

14. A method according to claim 10, wherein the supervised machine-learning classifier is a Support Vector Machine.

15. A method according to claim 10, further comprising a step of eliminating at least one feature from the feature set prior to classifying the item.

16. A computer-readable medium storing computer-executable process steps for machine-learning classification, said process steps comprising:

(a) obtaining an item having values for a plurality of different features in a feature set;

(b) obtaining scores for the different features, the score for a given feature being a measure of prediction ability for the given feature and having been calculated as a function of a plurality of different occurrence metrics pertaining to the given feature;

(c) scaling the values for the features according to the scores for said features, thereby obtaining adjusted feature set values for the item; and

(d) classifying the item by inputting the adjusted feature set values for the item into a previously trained classifier.

17. A computer-readable medium according to claim 16, wherein the given score is a measure of separation between the plurality of occurrence metrics, relative to an assumed probability distribution.

18. A computer-readable medium according to claim 16, wherein the plurality of occurrence metrics include a true positive rate for the given feature and a false positive rate for the given feature.

19. A computer-readable medium according to claim 18, wherein the score for the given feature is calculated as S=|aF⁻

1(tpr)−

bF<sup>−

1</sup>(fpr)|, where S is the score, tpr is the true positive rate of the given feature, fpr is the false positive rate of the given feature, |*| is an absolute value, F<sup>−

1</sup>(*) is an inverse of an assumed probability distribution function, and a and b are constants.

20. A computer-readable medium according to claim 16, wherein the values for the features are scaled such that ranges of values for the different features are proportionate to the scores corresponding to said different features.