METHOD FOR FEATURE SELECTION IN A SUPPORT VECTOR MACHINE USING FEATURE RANKING

• US 20080233576A1
• Filed: 10/30/2007
• Published: 09/25/2008
• Est. Priority Date: 05/01/1998
• Status: Active Grant

• Current Assignee: Curtis Anderson, Health Discovery Corporation, James Roberts, Joe Mckenzie, Jules B. Paderewski, Julian N. Stern, Memorial Health Systems Incorporated, Timothy P. O'Hayer
• Original Assignee: Curtis Anderson, Garry L. Carls, Glynn Bergeron, James Roberts, Joe Mckenzie, John E. Matthews, Jules B. Paderewski, Julian N. Stern, Memorial Health Systems Incorporated, Peter J. Farley, Simpson K. Russell, Timothy P. O'Hayer
• Inventors: Guyon, Isabelle, Perez-Cruz, Fernando, Weston, Jason, Scholkopf, Bernhard, Ellisseeff, Andre
• Granted Patent: US 7,805,388 B2
• Time in Patent Office: Days
• US Class Current: 435/6
• CPC Class Codes: C12Q 1/6883 for diseases caused by alterations of genetic material
• CPC Class Codes: C12Q 2600/112 Disease subtyping, staging or classification
• CPC Class Codes: C12Q 2600/158 Expression markers
• CPC Class Codes: G06F 18/2115 by evaluating different subsets according to an optimisation criterion, e.g. class separability, forward selection or backward elimination
• CPC Class Codes: G06F 18/2411 based on the proximity to a decision surface, e.g. support vector machines
• CPC Class Codes: G06N 20/10 using kernel methods, e.g. support vector machines [SVM]
• CPC Class Codes: G16B 25/00 ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
• CPC Class Codes: G16B 25/10 Gene or protein expression profiling; Expression-ratio estimation or normalisation
• CPC Class Codes: G16B 40/00 ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
• CPC Class Codes: G16B 40/20 Supervised data analysis
• CPC Class Codes: G16B 40/30 Unsupervised data analysis

Abstract

In a pre-processing step prior to training a learning machine, pre-processing includes reducing the quantity of features to be processed using feature selection methods selected from the group consisting of recursive feature elimination (RFE), minimizing the number of non-zero parameters of the system (l₀-norm minimization), evaluation of cost function to identify a subset of features that are compatible with constraints imposed by the learning set, unbalanced correlation score, transductive feature selection and single feature using margin-based ranking. The features remaining after feature selection are then used to train a learning machine for purposes of pattern classification, regression, clustering and/or novelty detection.

Claims

1. A computer-implemented method for selecting a subset of features for processing in a learning machine, wherein the features correspond to a dataset to be analyzed for patterns, the method comprising:

ranking the features according to a distance between extremal points of two classes of interest; and

selecting the subset of features having the highest rank.

2. The method of claim 1, wherein the learning machine is a support vector machine.

3. The method of claim 1, further comprising pre-processing the dataset by normalizing the data.

4. The method of claim 3, wherein the step of pre-processing comprises:

arranging the data in a matrix having rows and columns;

first normalizing the columns of the matrix;

second normalizing the rows of the matrix; and

third normalizing the columns of the matrix.

5. The method of claim 1, wherein the dataset comprises gene expression data obtained from DNA micro-arrays.

6. The method of claim 5, wherein the gene expression data is obtained from tissue from patients with renal cancer.

7. A computer-implemented method for selecting a subset of features for processing in a learning machine, wherein the features correspond to a dataset to be analyzed for patterns, the method comprising:

determining a margin between extremal points of two classes of interest; and

ranking the subset of features according to the size of the margin, wherein the largest margin corresponds to the highest rank.

8. The method of claim 7, wherein the learning machine is a support vector machine.

9. The method of claim 7, further comprising pre-processing the dataset by normalizing the data.

10. The method of claim 9, wherein the step of pre-processing comprises:

arranging the data in a matrix having rows and columns;

first normalizing the columns of the matrix;

second normalizing the rows of the matrix; and

third normalizing the columns of the matrix.

11. The method of claim 7, wherein the dataset comprises gene expression data obtained from DNA micro-arrays.

12. The method of claim 11, wherein the gene expression data is obtained from tissue from patients with renal cancer.

13. A method for diagnosing renal cancer in a patient comprising:

entering gene expression data into a computer adapted for implementing a learning machine, the gene expressions data comprising gene expression levels in tissue obtained from the patient for a gene selected from the group consisting of small inducible cytokine A2 (monocyte chemotactic protein

     1) and ATP synthase, H+ transporting, mitochondrial F1 complex, alpha subunit, isoform 1, cardiac muscle.