US 2004/0111384 A1Published application

System and method for generating micro-array data classification model using radial basis functions

OwnerMi Young Shin, Sun Hee Park +4inventors

InventorsMi Young Shin, Sun Hee Park, Sang Kyu Park, Kee-Wook Rim, Amrit L. Goel, Ho-Jung Rim

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Abstract

The present invention relates to a radial basis function classifier generating system and method to classify gene expression pattern appearing on micro-array for functional property. In the present invention, the ‘representation coverage’ to be represented by classifier and the ‘representation precision’, instead of various variables, are set to be input variables and other variables required to generate classifier are automatically determined based on the given values of the input variables. Developer's selection of the values of variables is minimized and the unnecessary trial-and-errors are reduced. Developers understand easily meaning of such input variables and can predict the result of the selection of variables. Accordingly, the trial-and-errors due to meaningless selection of the values of the variables are reduced, so the classifier generation process can be optimized.

Claims9

Dependent claims

A system of generating a micro-array data classifier using radial basis functions, the system comprising:

class learning data generating means for generating normalized learning data which include gene expression patterns on micro-array and their corresponding functional classes for samples;
learning data input variable setting means for setting input values for ‘representation coverage’ and ‘representation precision’ that are input variables to generate classifiers;
learning control variable/basis function width setting means for automatically setting a learning control variable and a basis function width to determine the classifier from the inputted ‘representation coverage’ and the inputted ‘representation precision’;
candidate classifier generating means for generating candidate classifier by automatically determining the number, centers and weights of the basis functions, which are parameters related to the radial basis function for the set learning control variables;
classifier validation means for computing validation error of a generated candidate classifier and checking if the generated candidate classifier has the minimal validation error;
and
classifier determining means for determining the classifier producing the minimal validation error among the candidate classifiers generated by the present invention as the final classifier.

4 more elements

A method of generating a micro-array data classifier using radial basis functions, the method comprising the steps of:

(a) generating the normalized of class learning data that include gene expression patterns on the micro-array;
(b) setting input values for ‘representation coverage’ and ‘representation precision’ that are input variables to generate classifier based on class learning data;
(c) setting a learning control variable and a basis function width to determine classifier from the ‘representation coverage’ and the ‘representation precision’;
(d) generating a candidate classifier by determining the number, centers and weights of the basis functions, which are parameters related to the radial basis function for the set learning control variables;
(e) computing validation error of the candidate classifier generated at the step (d) and checking if the generated candidate classifier has the minimal validation error;
(f) generating a candidate classifier by repeating the steps (d and e) with the basis function widths readjusted by ‘representation precision’;
and
(g) determining the classifier producing the minimal validation error as a final classifier.

5 more elements

Dependencies are inferred from the claim text.