Feature selection algorithm under conditions of noisy data and limited recording

• US 9,582,715 B2
• Filed: 06/30/2015
• Issued: 02/28/2017
• Est. Priority Date: 06/30/2015
• Status: Expired due to Fees

• Current Assignee: Kyndryl Incorporated
• Original Assignee: International Business Machines Corporation
• Inventors: Arunkumar, Saritha, Yang, Su
• Primary Examiner(s): NAKHJAVAN, SHERVIN K
• Application Number:
  US14/788,076
• Publication Number:
  US 20170004362A1
• Time in Patent Office: 609 Days
• Field of Search: None
• US Class Current: 1/1
• CPC Class Codes: G06F 18/2115 by evaluating different subsets according to an optimisation criterion, e.g. class separability, forward selection or backward elimination
• CPC Class Codes: G06F 18/214 Generating training patterns; Bootstrap methods, e.g. bagging or boosting
• CPC Class Codes: G06F 18/2148 characterised by the process organisation or structure, e.g. boosting cascade
• CPC Class Codes: G06F 18/241 relating to the classification model, e.g. parametric or non-parametric approaches
• CPC Class Codes: G06F 18/24137 Distances to cluster centroïds
• CPC Class Codes: G06F 2218/08 Feature extraction
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/047 Pattern matching networks; Rete networks

Abstract

Application of inter-class and intra-class filtering, based on aggregate point-to-point distances, to vector data for purposes of filtering the vector data for purposes of pattern recognition. In some embodiments: (i) the inter-class filtering is based on Euclidean distance, in all dimensions, between vector data points in vector space; and/or (ii) the intra-class filtering is based on a distance, in all dimensions, between vector data points in vector space.

Claims

1. A method comprising:

receiving, by machine logic of a computer, a set of vector data, wherein the set of vector data includes a plurality of vector values from each class of a plurality of classes;

applying, by machine logic of the computer, an inter-class filtering to the set of vector data, wherein the inter-class filtering is based, at least in part, on an inter-class distance; and

applying, by machine logic of the computer, an intra-class filtering to the set of vector data, wherein the intra-class filtering is based, at least in part, on an intra-class distance;

wherein;

the inter-class distance is based on a sum of distances between;

(i) a subject vector value in the plurality of vector values; and

(ii) at least some of the vector values in the plurality of vector values of at least one other class of the plurality of classes; and

the intra-class distance is based on a sum of distances between;

(i) a subject vector value in the plurality of vector values in a first class of the plurality of classes; and

(ii) at least some of the vector values in the plurality of vector values of the first class.

2. The method of claim 1, further comprising:

performing, by machine logic of the computer, a pattern recognition related action, wherein the pattern recognition related action is based, at least in part, on the inter-class filtering and the intra-class filtering.

3. The method of claim 2, wherein the performing, by machine logic of the computer, a pattern recognition related action includes:

assigning, by machine logic of the computer, a pattern to each class in the plurality of classes, wherein the pattern is based on;

the inter-class filtering or the intra-class filtering.

4. The method of claim 2, wherein the performing, by machine logic of the computer, a pattern recognition related action includes:

determining, by machine logic of the computer, a class to which an unknown vector value in the plurality of vector values belongs based, at least in part, on a previous pattern recognition related action.

5. The method of claim 1, wherein:

the inter-class distance is based on a sum of Euclidean distances between;

(i) a subject vector value in the plurality of vector values; and

(ii) at least some of the vector values in the plurality of vector values of at least one other class of the plurality of classes; and

the intra-class distance is based on a sum of Euclidean distances between;

(i) a subject vector value in the plurality of vector values in the first class of the plurality of classes; and

(ii) at least some of the vector values in the plurality of vector values of the first class.

6. The method of claim 1, further comprising:

applying, by machine logic of the computer, a density estimation to each class of the plurality of classes, wherein the density estimation is based, at least in part, on a determination of a representation for each class of the plurality of classes.

7. The method of claim 1 further comprising:

applying, by machine logic of the computer, a second inter-class filtering to the set of vector data, wherein the second inter-class filtering is based, at least in part, on a second inter-class distance.

8. A computer program product comprising a computer readable storage medium having stored thereon:

first program instructions programmed to receive a set of vector data, wherein the set of vector data includes a plurality of vector values from each class of a plurality of classes;

second program instructions programmed to apply an inter-class filtering to the set of vector data, wherein the inter-class filtering is based, at least in part, on an inter-class distance; and

third program instructions programmed to apply an intra-class filtering to the set of vector data, wherein the intra-class filtering is based, at least in part, on an intra-class distance;

wherein;

the inter-class distance is based on a sum of distances between;

(i) a subject vector value in the plurality of vector values; and

(ii) at least some of the vector values in the plurality of vector values of at least one other class of the plurality of classes; and

the intra-class distance is based on a sum of distances between;

(i) a subject vector value in the plurality of vector values in a first class of the plurality of classes; and

(ii) at least some of the vector values in the plurality of vector values of the first class.

9. The computer program product of claim 8, further comprising:

fourth program instructions programmed to perform a pattern recognition related action, wherein the pattern recognition related action is based, at least in part, on the inter-class filtering and the intra-class filtering.

10. The computer program product of claim 9, wherein the fourth program instruction programmed to perform a pattern recognition related action includes:

fifth program instruction programmed to assign a pattern to each class in the plurality of classes, wherein the pattern is based on;

the inter-class filtering or the intra-class filtering.

11. The computer program product of claim 9, wherein the fourth program instruction programmed to perform a pattern recognition related action includes:

fifth program instruction programmed to determine a class to which an unknown vector value in the plurality of vector values belongs based, at least in part, on a previous pattern recognition related action.

12. The computer program product of claim 8, wherein:

the inter-class distance is based on a sum of Euclidean distances between;

(i) a subject vector value in the plurality of vector values; and

(ii) at least some of the vector values in the plurality of vector values of at least one other class of the plurality of classes; and

the intra-class distance is based on a sum of Euclidean distances between;

(i) a subject vector value in the plurality of vector values in the first class of the plurality of classes; and

(ii) at least some of the vector values in the plurality of vector values of the first class.

13. The computer program product of claim 8, further comprising:

fourth program instructions programmed to apply a density estimation to each class of the plurality of classes, wherein the density estimation is based, at least in part, on a determination of a representation for each class of the plurality of classes.

14. A computer system comprising:

a processor set; and

a computer readable storage medium;

wherein;

the processor set is structured, located, connected, and/or programmed to run program instructions stored on the computer readable storage medium; and

the program instructions include;

first program instructions programmed to receive a set of vector data, wherein the set of vector data includes a plurality of vector values from each class of a plurality of classes;

second program instructions programmed to apply an inter-class filtering to the set of vector data, wherein the inter-class filtering is based, at least in part, on an inter-class distance; and

third program instructions programmed to apply an intra-class filtering to the set of vector data, wherein the intra-class filtering is based, at least in part, on an intra-class distance;

wherein;

the inter-class distance is based on a sum of distances between;

(i) a subject vector value in the plurality of vector values; and

(ii) at least some of the vector values in the plurality of vector values of at least one other class of the plurality of classes; and

the intra-class distance is based on a sum of distances between;

(i) a subject vector value in the plurality of vector values in a first class of the plurality of classes; and

(ii) at least some of the vector values in the plurality of vector values of the first class.

15. The computer system of claim 14, further comprising:

fourth program instructions programmed to perform a pattern recognition related action, wherein the pattern recognition related action is based, at least in part, on the inter-class filtering and the intra-class filtering.

16. The computer system of claim 15, wherein the fourth program instruction programmed to perform a pattern recognition related action includes:

fifth program instruction programmed to assign a pattern to each class in the plurality of classes, wherein the pattern is based on;

the inter-class filtering or the intra-class filtering.

17. The computer system of claim 15, wherein the fourth program instruction programmed to perform a pattern recognition related action includes:

fifth program instruction programmed to determine a class to which an unknown vector value in the plurality of vector values belongs based, at least in part, on a previous pattern recognition related action.

18. The computer system of claim 14, wherein:

the inter-class distance is based on a sum of Euclidean distances between;

(i) a subject vector value in the plurality of vector values; and

(ii) at least some of the vector values in the plurality of vector values of at least one other class of the plurality of classes; and

the intra-class distance is based on a sum of Euclidean distances between;

(i) a subject vector value in the plurality of vector values in the first class of the plurality of classes; and

(ii) at least some of the vector values in the plurality of vector values of the first class.

19. The computer system of claim 14, further comprising:

fourth program instructions programmed to apply a density estimation to each class of the plurality of classes, wherein the density estimation is based, at least in part, on a determination of a representation for each class of the plurality of classes.

20. The computer system of claim 14 further comprising:

fourth program instructions programmed to apply a second inter-class filtering to the set of vector data, wherein the second inter-class filtering is based, at least in part, on a second inter-class distance.