WO2016043846A4

WO2016043846A4 - A general formal concept analysis (fca) framework for classification

Info

Publication number: WO2016043846A4
Application number: PCT/US2015/041744
Authority: WO
Inventors: Michael J. O'brien; James BENVENUTO; Rajan Bhattacharyya
Original assignee: Hrl Laboratories Llc
Priority date: 2014-07-23
Filing date: 2015-07-23
Publication date: 2016-10-13
Also published as: EP3172701A4; CN106575380A; WO2016043846A3; WO2016043846A2; EP3172701A2; CN106575380B

Abstract

Described is a system for data classification using formal concept analysis (FCA). In a training phase, the system generates a FCA classification lattice, having a structure, using a set of training data. The set of training data comprises training presentations and classifications corresponding to the training presentations, in a classification phase, a set of test data having classes that are hierarchical in nature is classified using the structure of the FCA classification lattice.

Claims

AMENDED CLAIMS

received by the International Bureau on 29 August 2016 (29.08.2016)

What is claimed is:

A system for data classification using formal concept analysis (FCA), the system comprising:

one or more processors having associated memory with executable instructions encoded thereon such that when executed, the one or more processors perform operations of:

using the structure of a formal concept analysis (FCA)

classification lattice, generating a classification of a set of input data during a classification phase;

generating, in the classification phase, a presentation context vector, ffi_f!. from the set of test data, wherein ?Il is a set of attributes associated with a presentation p in the set of test data;

selecting, in the classification phase, a set of voting nodes in the FCA classification lattice; and

using, in the classification phase, the set of voting nodes to vote for a classification value for the presentation p.

generating, in a training phase, a context table from a set of training data, the context table having rows of object labels and columns of attribute labels; for each training presentation, appending, in the training phase, at least one class column for a classification corresponding to the training presentation to the context table; and

generating a FCA classification lattice from the context table. The system as set forth in Claim 2, wherein during generation of the FCA classification lattice the at least one class column is treated as a normal attribute, and wherein a sub-structure comprising a plurality of nodes within the FCA classification lattice that is spanned by a given class-attribute is associated with the corresponding classification.

4.

5. The system as set forth in Claim 1, wherein the set of voting nodes is selected according to a selection function operating on at least m_pand the FCA classification lattice.

6. The system as set forth in Claim 5, wherein a classification value, c, is voted on according to a voting function operating on at least the output of the selection function, the FCA classification lattice, and 17l_p.

7. The system as set forth in Claim 6, wherein the voting function returns a sum of an associated class value of each of the set of voting nodes. 8. The system as set forth in Claim 7, wherein each associated class value is

weighted by a number of attributes that it shares with the presentation p.

9. The system as set forth in Claim 6, wherein each voting node has an extent comprising a set of objects, and wherein the voting function returns a sum of an associated class value of each voting node, wherein the sum is normalized by a number of objects within the voting node's extent, wherein the normalized sums across all voting nodes are then summed.

10. The system as set forth in Claim 9, wherein each voting node has an intent

comprising a set of attributes, and wherein the associated class value for each voting node is weighted by a number of attributes in its intent.

11. The system as set forth in Claim 2, wherein the set of training data includes objects having attributes, and the FCA classification lattice is generated by treating the plurality of classifications as attributes of objects in the training data.

12. The system as set forth in Claim 1, wherein the set of input data is acquired using at least one of an fMRI sensor, an image sensor, and a sound sensor, and wherein the classification is performed for purposes of at least one of object recognition, image recognition, and sound recognition.

13. A computer-implemented method for data classification using formal concept analysis (FCA), the computer-implemented method using one or more processors to perform operations of:

using the structure of a FCA classification lattice, generating classification of a set of input data during a classification phase;

generating, in the classification phase, a presentation context vector, lJl_p, from the set of test data, wherein ?Il_p is a set of attributes associated with a presentation p in the set of test data;

14. A computer-implemented method for data classification using formal concept analysis (FCA), the computer-implemented method using one or more processors to perform operations of:

generating, in a training phase, a context table from a set of training data, the context table having rows of object labels and columns of attribute labels; for each training presentation, appending, in the training phase, at least one class column for a classification corresponding to the training presentation to the context table; and generating a FCA classification lattice from the context table.

15. The method as set forth in Claim 14, wherein during generation of the FCA

classification lattice the at least one class column is treated as a normal attribute, and wherein a sub-structure comprising a plurality of nodes within the FCA classification lattice that is spanned by a given class-attribute is associated with the corresponding classification.

16.

17. A computer program product for data classification using formal concept analysis (FCA), the computer program product comprising:

computer-readable instructions stored on a non-transitory computer- readable medium that are executable by a computer having one or more processors for causing the one or more processors to perform operations of:

using the structure of a FCA classification lattice, generating a classification of a set of input data during a classification phase; generating, in the classification phase, a presentation context vector, m_p, from the set of test data, wherein fH^, is a set of attributes associated with a presentation p in the set of test data;

18. A computer program product for data classification using formal concept analysis (FCA), the computer program product comprising:

computer-readable instructions stored on a non-transitory computer- readable medium that are executable by a computer having one or more processors for causing the one or more processors to perform operations of: generating, in a the training phase, a context table from a set of training data, the context table having rows of object labels and columns of attribute labels;

for each training presentation, appending, in the training phase, at least one class column for a classification corresponding to the training presentation to the context table; and

generating the FCA classification lattice from the context table.

19. The computer program product as set forth in Claim 18, wherein during

generation of the FCA classification lattice the at least one class column is treated as a normal attribute, and wherein a sub-structure comprising a plurality of nodes within the FCA classification lattice that is spanned by a given class-attribute is associated with the corresponding classification. 20.