Heuristic method of classification

• US 20070185824A1
• Filed: 04/13/2007
• Published: 08/09/2007
• Est. Priority Date: 06/19/2000
• Status: Active Grant

• Current Assignee: Vermillion, Inc.
• Original Assignee: Correlogic Systems, Inc. (Aspira Women's Health, Inc.)
• Inventors: Hitt, Ben
• Granted Patent: US 7,499,891 B2
• Time in Patent Office: Days
• US Class Current: 706/25
• CPC Class Codes: G06F 18/21 Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
• CPC Class Codes: G06F 18/211 Selection of the most significant subset of features
• CPC Class Codes: G06F 18/2433 Single-class perspective, e.g. one-against-all classification; Novelty detection; Outlier detection
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: Y10S 706/90 Fuzzy logic
• CPC Class Codes: Y10S 706/932 Mathematics, science, or engineering

Abstract

The invention concerns heuristic algorithms for the classification of Objects. A first learning algorithm comprises a genetic algorithm that is used to abstract a data stream associated with each Object and a pattern recognition algorithm that is used to classify the Objects and measure the fitness of the chromosomes of the genetic algorithm. The learning algorithm is applied to a training data set. The learning algorithm generates a classifying algorithm, which is used to classify or categorize unknown Objects. The invention is useful in the areas of classifying texts and medical samples, predicting the behavior of one financial market based on price changes in others and in monitoring the state of complex process facilities to detect impending failures.

Claims

1. A method of creating a classifying pattern for objects using a plurality of data strings, each data string associated with one of a plurality of objects to be classified, comprising:

selecting a set of data elements from each data string using a learning algorithm;

classifying the set of data elements using a pattern recognition algorithm; and

repeating the selecting and classifying with a different set of data elements selected from each data string until a classifying pattern is created that is acceptable to classify the objects.

2. The method of claim 1, wherein the learning algorithm is an evolutionary algorithm.

3. The method of claim 1, wherein the learning algorithm is a genetic algorithm.

4. The method of claim 1, wherein the data strings are produced by a high-throughput assay.

5. The method of claim 1, wherein the data strings are of a type are selected from the group consisting of:

(a) mass spectrometry data, (b) hybridization data, (c) gene expression data, (d) microarray data, (e) financial data, (f) stock market data, (g) text, (h) currency exchange rates, and (i) processing plant control status values.

6. The method of claim 1, wherein the objects are known to be of a first state or a second state and the model classifies objects by state.

7. The method of claim 1, wherein the objects are biological samples.

8. The method of claim 7, wherein classification of a sample provides information about a state selected from the group consisting of medical diagnosis and pathology.

9. The method of claim 7, wherein the sample is selected from the group of biological samples consisting of:

serum, plasma, and biopsy specimen.

10. The method of claim 1, wherein the pattern recognition algorithm is an adaptive pattern recognition algorithm.

11. The method of claim 10, wherein the pattern recognition algorithm creates a cluster map having a plurality of clusters associated with the set of data points.

12. The method of claim 11, wherein the acceptability of a grouping as a model to classify the objects is based on the homogeneity of the clusters in the cluster map.

13. The method of claim 12, wherein the model is the best lead cluster map.

14. The method of claim 11, wherein the grouping is acceptable as a model to classify the objects if a homogeneity of the cluster map is within a predetermined tolerance.

15. The method of claim 11, wherein the cluster map is created by

calculating a vector for each set of data points;

and

mapping the vectors into a vector space.

16. The method of claim 15, further comprising:

determining if a distance of at least one of the vectors from a closest preexisting centroid is within a predetermined threshold distance.

17. The method of claim 16, further comprising:

assigning the vector to a cluster associated with the preexisting centroid if the distance is within the predetermined threshold distance, and the assigning the vector includes adjusting the location of the preexisting centroid closer to the location of the vector.

18. The method of claim 16, wherein if the distance exceeds the predetermined threshold difference, defining a new centroid based on the location of the vector in the vector space, and if the distance is less than the threshold difference, assigning the vector to a cluster associated with the preexisting centroid.

19. A software product having a model constructed using the method of claim 1.

20. A model constructed using the method of claim 1.