FEATURE SELECTION

• US 20090157584A1
• Filed: 08/24/2006
• Published: 06/18/2009
• Est. Priority Date: 09/02/2005
• Status: Abandoned Application

• Current Assignee: Imperial Innovations Limited (Imperial College London)
• Original Assignee: Imperial Innovations Limited (Imperial College London)
• Inventors: Hu, Xiao-Peng, Yang, Guang-Zhong
• Application Number:
  US12/064,993
• Publication Number:
  US 20090157584A1
• Time in Patent Office: Days
• US Class Current: 706/46
• CPC Class Codes: G06F 18/2115 by evaluating different subsets according to an optimisation criterion, e.g. class separability, forward selection or backward elimination

Abstract

A method of feature selection applicable to both forward selection and backward elimination of features is provided. The method selects features to be used as an input for a classifier based on an estimate of the area under the ROC curve of each of the classifiers. Exemplary applications are in homecare or patient monitoring, body sensor networks, environmental monitoring, image processing and questionnaire design.

Claims

1. A method of automatically selecting features as an input to a classifier for a plurality of classes including calculating an estimate of the area under a receiver operating characteristic curve for each class of the classifier, and selecting the said features in dependence upon the said estimates.

2. A method as described in claim 1 in which the estimate is calculated in dependence upon an expected area under the curve calculated as a prior probability weighted sum of the area under the curve of each class.

3. A method as described in claim 2 in which the selecting includes starting with a set of features and repeatedly omitting a feature, the said feature being selected such that its omission results in the smallest change of the estimate for the resulting subset.

4. A method as described in claim 2 in which the selecting includes starting with an empty subset and repeatedly adding to the subset a feature, the said feature being selected such that its omission results in the largest change of the estimate for the resulting subset.

5. A method as claimed in claim 3 in which the change is estimated for each feature of the subset by considering the said feature and only a selection of the remaining features.

6. A method as claimed in claim 5 in which the change is calculated as a difference between the estimate of the expected area under the curve of the said selection of the remaining features and the said feature and the estimate of the expected area under the curve of the said selection of remaining features.

7. A method as claimed in claim 5 in which the method includes calculating a respective differential measure of the said feature and each remaining feature in the subset and choosing a predetermined number of the remaining features having the smallest respective differential measure for the said selection.

8. A method as claimed in claim 7 in which the respective differential measure is the difference in the estimate of the expected area under the curve for the said feature and the estimate of the expected area under the curve for the said feature and the respective remaining feature.

9. A method as claimed in claim 7 in which the differential measure is calculated for all features of the set prior to selecting any of the features.

10. A method as claimed in claim 3, in which features are added to or omitted from the subset until the subset includes a predetermined number of features.

11. A method as claimed in claim 3 in which features are added to or omitted from the subset until the estimate reaches a desired level.

12. A method as claimed in claim 1 in which one or more features are derived from one or more channels from one or more sensors.

13. A method as claimed in claim 12 in which the sensors include environmental sensors measuring quantities indicative of air, water or soil quality.

14. A method as claimed in claim 1 in which one or more features are derived from a digital image by image processing.

15. A method as claimed in claim 14, the derived features being representative of texture orientations, patterns or colours in the image.

16. A method as claimed in claim 1 in which one or more features are representative of the activity of a biomarker.

17. A method as claimed in claim 16 in which the activity of the biomarker is representative of the presence or absence of a target associated with the biomarker.

18. A method as claimed in claim 17, in which the target is a nucleic acid, a peptide, a protein, a virus or an antigen.

19. A method as claimed in claim 1, in which the features include questions in an opinion poll or survey.

28. A method as claimed in claim 4 in which the change is estimated for each feature of the subset by considering the said feature and only a selection of the remaining features.

29. A method as claimed in claim 6 in which the method includes calculating a respective differential measure of the said feature and each remaining feature in the subset and choosing a predetermined number of the remaining features having the smallest respective differential measure for the said selection.

30. A method as claimed in claim 8 in which the differential measure is calculated for all features of the set prior to selecting any of the features.

20. A method of defining a sensor network of a plurality of sensors in an environment including acquiring a data set of features corresponding to the sensors and selecting features as an input to a classifier for a plurality of classes including calculating an estimate of the area under a receiver operating characteristic curve for each class of the classifier, and selecting the said features in dependence upon the said estimates.

21. A method as claimed in claim 20, including removing from the environment any sensors corresponding to features not selected.

22. A sensor network of a plurality of sensors in an environment by the process of:

acquiring a data set of features corresponding to the sensors and selecting features as an input to a classifier for a plurality of classes including calculating an estimate of the area under a receiver operating characteristic curve for each class of the classifier, and selecting the said features in dependence upon the said estimates.

23. A homecare or patient monitoring environment including a sensor network of a plurality of sensors in an environment defined by the process of:

acquiring a data set of features corresponding to the sensors and selecting features as an input to a classifier for a plurality of classes including calculating an estimate of the area under a receiver operating characteristic curve for each class of the classifier, and selecting the said features in dependence upon the said estimates.

24. A body sensor network including a sensor network of a plurality of sensors in an environment defined by the process of:

acquiring a data set of features corresponding to the sensors and selecting features as an input to a classifier for a plurality of classes including calculating an estimate of the area under a receiver operating characteristic curve for each class of the classifier, and selecting the said features in dependence upon the said estimates.

25. A computer system arranged to implement a method comprising:

automatically selecting features as an input to a classifier for a plurality of classes including calculating an estimate of the area under a receiver operating characteristic curve for each class of the classifier, and selecting the said features in dependence upon the said estimates.

26. (canceled)

27. A computer readable storage medium carrying a computer program which when executed by one or more processors causes the one or more processors to perform:

automatically selecting features as an input to a classifier for a plurality of classes including calculating an estimate of the area under a receiver operating characteristic curve for each class of the classifier, and selecting the said features in dependence upon the said estimates.