Configurable Machine Learning Method Selection and Parameter Optimization System and Method

• US 20160110657A1
• Filed: 10/14/2015
• Published: 04/21/2016
• Est. Priority Date: 10/14/2014
• Status: Abandoned Application

• Current Assignee: Skytree, Inc (Infosys Limited)
• Original Assignee: Skytree, Inc (Infosys Limited)
• Inventors: Gibiansky, Maxsim, Riegel, Ryan, Yang, Yi, Ram, Parikshit, Gray, Alexander
• Application Number:
  US14/883,522
• Publication Number:
  US 20160110657A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06N 20/00 Machine learning

Abstract

A system and method for selecting a machine learning method and optimizing the parameters that control its behavior including receiving data; determining, using one or more processors, a first candidate machine learning method; tuning, using one or more processors, one or more parameters of the first candidate machine learning method; determining, using one or more processors, that the first candidate machine learning method and a first parameter configuration for the first candidate machine learning method are the best based on a measure of fitness subsequent to satisfaction of a stop condition; and outputting, using one or more processors, the first candidate machine learning method and the first parameter configuration for the first candidate machine learning method.

Claims

1. A method comprising:

receiving data;

determining, using one or more processors, a first candidate machine learning method;

tuning, using one or more processors, one or more parameters of the first candidate machine learning method;

determining, using one or more processors, that the first candidate machine learning method and a first parameter configuration for the first candidate machine learning method are the best based on a measure of fitness subsequent to satisfaction of a stop condition; and

outputting, using one or more processors, the first candidate machine learning method and the first parameter configuration for the first candidate machine learning method.

2. The method of claim 1 further comprising:

determining a second machine learning method;

tuning, using one or more processors, one or more parameters of the second candidate machine learning method, the second candidate machine learning method differing from the first candidate machine learning method; and

wherein the determination that the first candidate machine learning method and the first parameter configuration for the first candidate machine learning method are the best based on the measure of fitness includes determining that the first candidate machine learning method and the first parameter configuration for the first candidate machine learning method provide superior performance with regard to the measure of fitness when compared to the second candidate machine learning method with the second parameter configuration.

3. The method of claim 2, wherein the tuning of the one or more parameters of the first candidate machine learning method is performed using a first processor of the one or more processors and the tuning of the one or more parameters of the second candidate machine learning method is performed using a second processor of the one or more processors in parallel with the tuning of the first candidate machine learning method.

4. The method of claim 2, wherein a first processor of the one or more processors communicates with a second processor of the one or more processors in order to update the second processor'"'"'s previously learned parameter distribution with a result of the first processor'"'"'s tuning, wherein the result of the first processor'"'"'s tuning is one of an intermediate and a complete tuning result.

5. The method of claim 2, wherein a greater portion of the resources of the one or more processors is dedicated to tuning the one or more parameters of the first candidate machine learning method than to tuning the one or more parameters of the second candidate machine learning method based on tuning already performed on the first candidate machine learning method and the second candidate machine learning method, the tuning already performed indicating that the first candidate machine learning method is performing better than the second machine learning method based on the measure of fitness.

6. The method of claim 2, wherein the user specifies the data, and wherein the first candidate machine learning method and the second machine learning method are determined and the tunings and determination that the first candidate machine learning method and a first parameter configuration for the first candidate machine learning method are the best based on a measure of fitness are performed automatically without user-provided information or with user-provided information.

7. The method of claim 1, wherein tuning the one or more parameters of the first candidate machine learning method further comprises:

setting a prior parameter distribution;

generating a set of sample parameters for the one or more parameters of the first candidate machine learning method based on the prior parameter distribution;

forming a new parameter distribution based on the prior parameter distribution and the previously generated set of sample parameters for each of the one or more parameters of the first candidate;

generating a new set of sample parameters for the one or more parameters of the first candidate machine learning method.

8. The method of claim 7, the method further comprising:

determining the stop condition is not met;

setting the new parameter distribution as a previously learned parameter distribution and setting the new set of sample parameters as the previously generated set of sample parameters; and

repeatedly forming a new parameter distribution based on the previously learned parameter distribution and the previously generated sample parameters for each of the one or more parameters of the first candidate machine learning candidate, generating a new set of sample parameters for the one or more parameters of the first candidate machine learning method, setting the new parameter distribution as the previously learned parameter distribution and setting the new set of sample parameters as the previously generated set of sample parameters before the stop condition is met.

9. The method of claim 7, wherein one or more of the determination of the first candidate machine learning method and the tuning of the one or more parameters of the first candidate machine learning method are based on a previously learned parameter distribution.

10. The method of claim 1, wherein the received data includes at least a portion of a Big Data data set and wherein the tuning of the one or more parameters of the first candidate machine learning method is based on the Big Data data set.

11. A system comprising:

one or more processors; and

a memory storing instructions that, when executed by the one or more processors, cause the system to;

receive data;

determine a first candidate machine learning method;

tune one or more parameters of the first candidate machine learning method;

determine that the first candidate machine learning method and a first parameter configuration for the first candidate machine learning method are the best based on a measure of fitness subsequent to satisfaction of a stop condition; and

output the first candidate machine learning method and the first parameter configuration for the first candidate machine learning method.

12. The system of claim 11, the memory storing instructions that, when executed by the one or more processors, cause the system to:

determine a second machine learning method;

tune one or more parameters of the second candidate machine learning method, the second candidate machine learning method differing from the first candidate machine learning method; and

wherein the determination that the first candidate machine learning method and the first parameter configuration for the first candidate machine learning method are the best based on the measure of fitness includes determining that the first candidate machine learning method and the first parameter configuration for the first candidate machine learning method provide superior performance with regard to the measure of fitness when compared to the second candidate machine learning method with the second parameter configuration.

13. The system of claim 12, wherein the tuning of the one or more parameters of the first candidate machine learning method is performed using a first processor of the one or more processors and the tuning of the one or more parameters of the second candidate machine learning method is performed using a second processor of the one or more processors in parallel with the tuning of the first candidate machine learning method.

14. The system of claim 12, wherein a first processor of the one or more processors alternates between the tuning the one or more parameters of the first candidate machine learning method and the tuning of the one or more parameters of the second candidate machine learning method.

15. The system of claim 12, wherein a greater portion of the resources of the one or more processors is dedicated to tuning the one or more parameters of the first candidate machine learning method than to tuning the one or more parameters of the second candidate machine learning method based on tuning already performed on the first candidate machine learning method and the second candidate machine learning method, the tuning already performed indicating that the first candidate machine learning method is performing better than the second machine learning method based on the measure of fitness.

16. The system of claim 12, wherein the user specifies the data, and wherein the first candidate machine learning method and the second machine learning method are selected and the tunings and determination are performed automatically without user-provided information or with user-provided information.

17. The system of claim 11, wherein tuning the one or more parameters of the first candidate machine learning method further comprises:

setting a prior parameter distribution;

generating a set of sample parameters for the one or more parameters of the first candidate machine learning method based on the prior parameter distribution;

forming a new parameter distribution based on the prior parameter distribution and the previously generated set of sample parameters for each of the one or more parameters of the first candidate;

generating a new set of sample parameters for the one or more parameters of the first candidate machine learning method.

18. The system of claim 17, the memory storing instructions that, when executed by the one or more processors, cause the system to:

determine the stop condition is not met;

set the new parameter distribution as a previously learned parameter distribution and setting the new set of sample parameters as the previously generated set of sample parameters; and

repeatedly form a new parameter distribution based on the previously learned parameter distribution and the previously generated sample parameters for each of the one or more parameters of the first candidate machine learning candidate, generate a new set of sample parameters for the one or more parameters of the first candidate machine learning method, set the new parameter distribution as the previously learned parameter distribution and set the new set of sample parameters as the previously generated set of sample parameters before the stop condition is met.

19. The system of claim 17, wherein one or more of the determination of the first candidate tuning method and the tuning of the one or more parameters of the first candidate machine learning method are based on a previously learned parameter distribution.

20. The system of claim 11, wherein the received data includes at least a portion of a Big Data data set and wherein the tuning of the one or more parameters of the first candidate machine learning method is based on the Big Data data set.