A METHOD FOR MODELLING A HUGE AMOUNT OF DATA

摘要

A modeling method of high capacity data is provided to select representative data aggregate from high capacity data by using a genetic algorithm and to obtain an optimal hyperplane by applying a KRR(Kernel Ridge Regression) method to the selected representative data aggregate, consequently data modeling of high capacity data can be accomplished efficiently by using the small amount of calculation. A method for modeling high capacity data comprises the steps of: selecting representative data aggregate(415) by applying a genetic algorithm to high capacity data(410); extracting a feature vector(425) from the selected data aggregate(420); obtaining an optimal hyperplane(435), that is a data model, by applying KRR to the extracted feature vectors(430). In particular, the number of data which belong to the representative data aggregate can be set up optionally in the process to which the genetic algorithm is applied, and the trade-off between the accuracy of the optimal hyperplane and the calculation amount which is required from the step 430 can be adjusted by the optional setting. In addition, the feature vectors extracted from the step 420 which is the feature vector extraction step can be expanded to nonlinear space by using a Kernel function prior to the step 430 to which the KRR is applied.