CHOOSING PATTERN RECOGNITION ALGORITHMS AND DATA FEATURES

• US 20130073490A1
• Filed: 09/16/2011
• Published: 03/21/2013
• Est. Priority Date: 09/16/2011
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Baughman, Aaron K., Perlitz, Michael, Van Der Stockt, Stefan A. G.
• Granted Patent: US 8,838,510 B2
• Time in Patent Office: Days
• US Class Current: 706/13
• CPC Class Codes: G06N 3/126 Evolutionary algorithms, e.g. genetic algorithms or genetic programming

Abstract

A system, method and program product for selecting an algorithm and feature set to solve a problem. A perpetual analytics system is disclosed that provides a genetic algorithm for jointly selecting an algorithm and feature set to solve a problem, comprising: an evolutionary computing engine for processing data encoded as chromosomes, wherein each chromosome encodes an algorithm and a feature set; a domain knowledge store that maintains a plurality of algorithms and a plurality of features; a system for applying a generation of chromosomes to a set of data to provide a set of results; and a fitness function for evaluating the set of results to rate a performance of each chromosome in the set of chromosomes; wherein the evolutionary computing engine is adapted to evolve a subset of the set of chromosomes into a new generation of chromosomes.

Claims

1. A perpetual analytics system that provides a genetic algorithm for selecting an algorithm and feature set to solve a problem, comprising:

an evolutionary computing engine for processing data encoded as chromosomes, wherein each chromosome encodes an algorithm and a feature set;

a domain knowledge store that maintains a plurality of algorithms and a plurality of features;

a system for applying a generation of chromosomes to a set of data to provide a set of results; and

a fitness function for evaluating the set of results to rate a performance of each chromosome in the set of chromosomes;

wherein the evolutionary computing engine is adapted to evolve a subset of the set of chromosomes into a new generation of chromosomes.

2. The perpetual analytics system of claim 1, wherein the evolutionary computing system comprises a Evolutionary Computing in Java (ECJ) infrastructure.

3. The perpetual analytics system of claim 1, wherein each chromosome is encoded as binary data.

4. The perpetual analytics system of claim 1, further comprising a training system.

5. The perpetual analytics system of claim 1, further comprising a system for determining a set of parameters for the genetic algorithm.

6. The perpetual analytics system of claim 1, further comprising:

a system for implementing an exit criteria to stop evolving chromosomes; and

a system for outputting a best algorithm and feature set to solve an inputted problem.

7. A method of selecting an algorithm and feature set to solve a problem, comprising:

providing an initial generation of chromosomes, wherein each chromosome encodes an algorithm and a feature set;

applying each chromosome from the initial generation of chromosomes to a set of data to provide a set of results;

evaluating the set of results with a fitness function to rate a performance of each chromosome in the initial set of chromosomes; and

evolving a subset of chromosomes to creates a new generation of chromosomes.

8. The method of claim 7, wherein the initial set of chromosomes is determined based on a domain knowledge store.

9. The method of claim 7, wherein evolving a subset of chromosomes is implemented with an evolutionary computing engine.

10. The method of claim 7, further comprising:

applying each chromosome from the new generation of chromosomes to the set of data to provide a new set of results;

evaluating the new set of results with the fitness function to rate a performance of each chromosome in the new set of chromosomes;

evolving a subset of chromosomes to creates a next generation of chromosomes; and

repeating the applying evaluating and evolving steps until an exit criteria is met.

11. The method of claim 10, further comprising:

outputting a best algorithm and feature set to solve the problem based on the performance of an associated chromosome.

12. The method of claim 7, further comprising determining a set of genetic algorithm parameters.

13. The method of claim 7, wherein the evolving is selected from a group consisting of:

mutating, cross over, and reproduction.

14. A program product stored on a computer readable storage medium for selecting an algorithm and feature set to solve a problem, comprising:

program code for providing an initial generation of chromosomes, wherein each chromosome encodes an algorithm and a feature set;

program code for applying each chromosome from the initial generation of chromosomes to a set of data to provide a set of results;

program code for evaluating the set of results with a fitness function to rate a performance of each chromosome in the initial set of chromosomes; and

program code for evolving a subset of chromosomes to creates a new generation of chromosomes.

15. The program product of claim 14, wherein the initial set of chromosomes is determined based on a domain knowledge store.

16. The program product of claim 14, wherein evolving a subset of chromosomes is implemented with an evolutionary computing engine.

17. The program product of claim 14, further comprising:

program code for applying each chromosome from the new generation of chromosomes to the set of data to provide a new set of results;

program code for evaluating the new set of results with the fitness function to rate a performance of each chromosome in the new set of chromosomes;

program code for evolving a subset of chromosomes to creates a next generation of chromosomes; and

program code for repeating the applying evaluating and evolving processes until an exit criteria is met.

18. The program product of claim 17, further comprising:

program code for outputting a best algorithm and feature set to solve the problem based on the performance of an associated chromosome.

19. The program product of claim 14, further comprising program code for determining a set of genetic algorithm parameters.

20. The program product of claim 14, wherein the evolving is selected from a group consisting of:

mutating, cross over, and reproduction.