Method and computer program product for determining an efficient feature set and an optimal threshold confidence value for a pattern recogniton classifier

• US 20040096107A1
• Filed: 11/14/2002
• Published: 05/20/2004
• Est. Priority Date: 11/14/2002
• Status: Abandoned Application

• Current Assignee: Lockheed Martin Corporation (Martin Marietta Corporation)
• Original Assignee: Lockheed Martin Corporation (Martin Marietta Corporation)
• Inventors: Ii, David L., Reitz, Elliott D. II, Tillotson, Dennis A.
• Application Number:
  US10/294,534
• Publication Number:
  US 20040096107A1
• Time in Patent Office: Days
• US Class Current: 382/224
• CPC Class Codes: G06F 18/211 Selection of the most significant subset of features

Abstract

A method and computer program product are disclosed for determining an efficient set of features and an optimal confidence threshold value for a pattern recognition system with at least one output class. An initial set of features is selected based upon an optimization algorithm. A plurality of pattern samples are then classified using the selected feature set. A threshold confidence value is optimized as to maximize the accuracy of the classification. The selected feature set and threshold confidence value are accepted if a cost function based upon classification accuracy meets a predetermined threshold cost function value. The feature set is changed, by adding, removing or replacing a feature within the set based upon the optimization algorithm, if the cost function does not meet the predetermined threshold cost function value.

Claims

1. A method of determining an efficient set of features and an optimal threshold confidence value for a pattern recognition system with at least one output class, comprising:

selecting an initial set of features based upon an optimization algorithm;

classifying a plurality of pattern samples using the selected feature set;

optimizing a threshold confidence value to maximize the accuracy of the classification;

accepting the selected feature set and threshold confidence value if a cost function based upon classification accuracy meets a predetermined threshold cost function value; and

changing the feature set, by adding, removing or replacing a feature within the set based upon the optimization algorithm, if the cost function does not meet the predetermined threshold cost function value.

2. A method as set forth in claim 1, wherein the step of selecting an initial set of features according to an optimization algorithm includes the use of a genetic selection algorithm.

3. A method as set forth in claim 1, wherein the step of selecting an initial set of features according to an optimization algorithm includes the use of a discrete gradient search technique.

4. A method as set forth in claim 1, wherein the step of optimizing the threshold value includes the use of a genetic selection algorithm.

5. A method as set forth in claim 1, wherein the step of optimizing the threshold confidence value includes the use of a gradient search algorithm.

6. A method as set forth in claim 1, wherein the step of classifying a plurality of pattern samples includes the use of a two-stage compound classifier.

7. A method as set forth in claim 1, wherein said cost function is calculated as the sum of the multiplicative product of the time necessary to complete a classification and a first factor and the multiplicative product of an error rate for the classification and a second factor.

8. A method as set forth in claim 1, wherein the plurality of pattern samples includes scanned images.

9. A method as set forth in claim 8, wherein at least of the one output class(es) represents an alphanumeric character.

10. A method as set forth in claim 8, wherein at least one of the output class(es) represents a type of postal indicia.

11. A computer program product for determining an efficient set of features and an optimal threshold confidence value for a pattern recognition system with at least one output class, comprising:

a selection portion that selects an initial set of features based upon an optimization algorithm;

a classification portion that classifies a plurality of pattern samples using the selected feature set;

a threshold optimization portion that optimizes a threshold confidence value to maximize the accuracy of the classification; and

an evaluation portion that accepts the selected feature set and threshold confidence value if a cost function based upon classification accuracy meets a predetermined cost function threshold and changes the feature set, by adding, removing or replacing a feature within the set based upon the optimization algorithm, if the cost function does not meet the predetermined cost function threshold.

12. A computer program product as set forth in claim 11, wherein the selection portion uses a genetic selection algorithm to select an initial set of features.

13. A computer program product as set forth in claim 11, wherein the selection portion uses a discrete gradient search technique to select an initial set of features.

14. A computer program product as set forth in claim 11, wherein the threshold optimization portion uses a genetic selection algorithm.

15. A computer program product as set forth in claim 11, wherein the threshold optimization portion uses a discrete gradient search technique.

16. A computer program product as set forth in claim 11, wherein the classification portion uses a two-stage compound classifier.

17. A computer program product as set forth in claim 11, wherein said cost function is calculated as the sum of the multiplicative product of the time necessary to complete a classification and a first factor and the multiplicative product of an error rate for the classification and a second factor.

18. A computer program product as set forth in claim 11, wherein the plurality of pattern samples includes scanned images.

19. A computer program product as set forth in claim 18, wherein at least one of the output class(es) represents an alphanumeric character.

20. A computer program product as set forth in claim 18, wherein at least one of the output class(es) represents a type of postal indicia.