Refined learning data representation for classifiers

Patent metadata

Field	Value
Publication number	US-10504038-B2
Application number	US-201615143792-A
Country	US
Kind code	B2
Filing date	May 2, 2016
Priority date	May 2, 2016
Publication date	Dec 10, 2019
Grant date	Dec 10, 2019

Abstract

In one embodiment, a learning machine device initializes thresholds of a data representation of one or more data features, the thresholds specifying a first number of pre-defined bins (e.g., uniform and equidistant bins). Next, adjacent bins of the pre-defined bins having substantially similar weights may be reciprocally merged, the merging resulting in a second number of refined bins that is less than the first number. Notably, while merging, the device also learns weights of a linear decision rule associated with the one or more data features. Accordingly, a data-driven representation for a data-driven classifier may be established based on the refined bins and learned weights.

First claim

What is claimed is:

1. A method, comprising:
initializing, at a learning machine device, thresholds of a data representation of one or more data features, the thresholds specifying a first number of pre-defined bins;
reciprocally merging adjacent bins of the pre-defined bins having substantially similar weights, the merging resulting in a second number of refined bins that is less than the first number;
simultaneously learning weights of a linear decision rule associated with the one or more data features while merging; and
establishing a data-driven representation for a data-driven classifier based on the refined bins and learned weights.

2. The method as in claim 1 , wherein the pre-defined bins are uniform and equidistant.

3. The method as in claim 1 , further comprising:
using the data-driven classifier on traffic in a computer network.

4. The method as in claim 1 , further comprising:
sharing the data-driven classifier with one or more other devices.

5. The method as in claim 1 , wherein the data-driven classifier is a linear support vector machine (SVM).

6. The method as in claim 1 , wherein substantially similar weights comprise one or more of equal weights, similar weights, and weights having a same sign.

7. The method as in claim 1 , wherein the one or more features comprise a feature pair, and wherein correlation between feature values of the feature pair is used for the data representation.

8. The method as in claim 1 , wherein the one or more features are binarized.

9. The method as in claim 1 , wherein the one or more features are real-valued.

10. An apparatus, comprising:
a processor configured to execute one or more processes; and
a memory configured to store a process executable by the processor, the process when executed operable to:
initialize thresholds of a data representation of one or more data features, the thresholds specifying a first number of pre-defined bins;
reciprocally merge adjacent bins of the pre-defined bins having substantially similar weights, the merging resulting in a second number of refined bins that is less than the first number;
simultaneously learn weights of a linear decision rule associated with the one or more data features while merging; and
establish a data-driven representation for a data-driven classifier based on the refined bins and learned weights.

11. The apparatus as in claim 10 , wherein the pre-defined bins are uniform and equidistant.

12. The apparatus as in claim 10 , wherein the process when executed is further operable to:
use the data-driven classifier on traffic in a computer network.

13. The apparatus as in claim 10 , wherein the process when executed is further operable to:
share the data-driven classifier with one or more other devices.

14. The apparatus as in claim 10 , wherein the data-driven classifier is a linear support vector machine (SVM).

15. The apparatus as in claim 10 , wherein substantially similar weights comprise one or more of equal weights, similar weights, and weights having a same sign.

16. The apparatus as in claim 10 , wherein the one or more features comprise a feature pair, and wherein correlation between feature values of the feature pair is used for the data representation.

17. The apparatus as in claim 10 , wherein the one or more features are binarized.

18. The apparatus as in claim 10 , wherein the one or more features are real-valued.

19. A tangible, non-transitory, computer-readable media having software encoded thereon, the software when executed by a processor operable to:
initialize thresholds of a data representation of one or more data features, the thresholds specifying a first number of pre-defined bins;
reciprocally merge adjacent bins of the pre-defined bins having substantially similar weights, the merging resulting in a second number of refined bins that is less than the first number;
simultaneously learn weights of a linear decision rule associated with the one or more data features while merging; and
establish a data-driven representation for a data-driven classifier based on the refined bins and learned weights.

20. The computer-readable media as in claim 19 , wherein the pre-defined bins are uniform and equidistant.

Assignees

• Cisco Tech Inc

Inventors

• Franc Vojtech
• Bartos Karel
• Sofka Michal

Classifications

• G06F21/552

  involving long-term monitoring or reporting · CPC title

• G06F17/11

  for solving equations {, e.g. nonlinear equations, general mathematical optimization problems (optimization specially adapted for a specific administrative, business or logistic context G06Q10/04)} · CPC title

• H04L63/1425

  Traffic logging, e.g. anomaly detection · CPC title

• G06N20/00 Primary

  Machine learning · CPC title

• G06N20/10 Primary

  using kernel methods, e.g. support vector machines [SVM] · CPC title