Method and system for feature selection in classification

• US 20070168306A1
• Filed: 01/17/2006
• Published: 07/19/2007
• Est. Priority Date: 01/17/2006
• Status: Abandoned Application

• Current Assignee: Agilent Technologies, Inc.
• Original Assignee: Agilent Technologies, Inc.
• Inventors: Smith, David, Li, Jonathan
• Application Number:
  US11/334,061
• Publication Number:
  US 20070168306A1
• Time in Patent Office: Days
• US Class Current: 706/13
• CPC Class Codes: G06F 18/2111 by using evolutionary computational techniques, e.g. genetic algorithms
• CPC Class Codes: G06N 3/126 Evolutionary algorithms, e.g. genetic algorithms or genetic programming

Abstract

Individuals in a population are paired together to produce children. Each individual has a subset of features obtained from a group of features. A genetic algorithm is used to construct combinations or subsets of features in the children. A classification algorithm is then used to evaluate the fitness or cost value of each child. The processes of reproduction and evaluation repeat until the population reaches a given classification level. A different classification algorithm is then applied to the population that reached the given classification level.

Claims

1. A method for feature selection in classification in quality assurance testing, the method comprising:

a) applying a genetic algorithm to a pairs of individuals in a population to produce a generation of children, wherein each child is comprised of a combination of features constructed from a respective pair of individuals; and

b) applying a first classification algorithm to the generation of children to determine a cost function for each child.

2. The method of claim 1, further comprising repeating a) and b) until a present generation of children reaches a given classification level.

3. The method of claim 2, wherein repeating a) and b) until a present generation of children reaches a given classification level comprises repeating a) and b) until a present generation of children reaches stasis.

4. The method of claim 2, further comprising:

c) applying a second classification algorithm to the present generation of children that reached the given classification level.

5. The method of claim 1, wherein applying a first classification algorithm to the generation of children to determine a cost function for each child comprises applying a Gaussian maximum likelihood classification algorithm to the generation of children to determine a cost function for each child.

6. The method of claim 4, wherein applying a second classification algorithm to the present generation of children comprises applying a k nearest neighbor classification algorithm to the present generation of children that reached the given classification level.

7. A method for feature selection in classification for use in quality assurance testing, comprising:

a) creating a generation of children from a population comprised of a first plurality of individuals, wherein each child is comprised of a combination of features constructed from a respective pair of individuals;

b) applying a first classification algorithm to the generation of children to evaluate a cost function for each child;

c) creating a subsequent generation of children differing from the previous generation of children;

d) repeating b) and c) until a present generation of children reaches a given classification level; and

e) when the present generation of children reaches the given classification level, applying a second classification algorithm to the present generation of children.

8. The method of claim 7, further comprising applying one or more genetic operators to a subsequent generation of children.

9. The method of claim 7, further comprising selecting pairs of individuals in the first plurality of individuals by randomly selecting pairs of individuals.

10. The method of claim 7, further comprising selecting pairs of individuals in the first plurality of individuals based on a cost function of each individual relative to the others in the first plurality of individuals.

11. The method of claim 7, wherein applying a first classification algorithm to the generation of children to evaluate a cost function for each child comprises applying a Gaussian maximum likelihood classification algorithm to the generation of children to evaluate a cost function for each child.

12. The method of claim 7, wherein applying a second classification algorithm to the present generation of children comprises applying a k nearest neighbor classification algorithm to the present generation of children that reached the given classification level.

13. The method of claim 7, wherein repeating b) and c) until a present generation of children reaches a given classification level comprises repeating b) and c) until a present generation of children reaches stasis.

14. A system for feature selection in classification for quality assurance testing, comprising:

an input device operable to obtain a plurality of features from an object; and

a processor operable to perform feature selection in classification using the plurality of features, wherein the performance of feature selection in classification includes the application of two classification algorithms.

15. The system of claim 14, further comprising memory for storing one or more known feature sets.

16. The system of claim 15, wherein the processor is operable to apply a genetic algorithm to the plurality of features to produce subsets of features.

17. The system of claim 15, wherein one of the two classification algorithms comprises a Gaussian maximum likelihood classification algorithm.

18. The system of claim 15, wherein one of the two classification algorithms comprises a k nearest neighbor classification algorithm.

19. The system of claim 15, wherein the input device comprises an imager.