Heuristic method of classification

• US 20060112041A1
• Filed: 11/15/2005
• Published: 05/25/2006
• 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 7,240,038 B2
• Time in Patent Office: Days
• US Class Current: 706/20
• 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 for creating a model for classifying a biological sample as being of a first state or a second state different than the first state, comprising:

determining the location of a first set of vectors and a second set of vectors in a vector space, each vector of the first set of vectors being obtained from a data stream derived from a biological sample known to be of the first state, the second set of vectors including a plurality of vectors, each of the plurality of vectors of the second set of vectors being associated with a data stream derived from a biological sample known to be of the second state; and

identifying a cluster disposed within the vector space, the cluster containing at least one of the vectors of the first set of vectors, the cluster being associated with the first state for purposes of classifying a biological sample.