Modeling method for support vector machine based on data compression

Abstract

The present invention relates to a modeling method for a support vector machine based on data compression. The modeling method has the technical characteristics that the method comprises the following steps: sampling modeling data through an equidistant sampling method; compressing the modeling data; calculating the boundary of each cluster of data under leaf nodes of a clustering feature tree, and choosing a boundary point most possibly becoming a support vector as the modeling data of the support vector machine; and establishing a model of the support vector machine: establishing a model of the support vector machine according to the modeling data through a support vector machine method. In the modeling method of the present invention, the modeling sample quantity of the support vector machine is greatly reduced under the condition of ensuring the accuracy rate of the algorithm to the greatest extent through a pre-sampling strategy, a data compression technology, an increment sampling strategy and the like, so as to greatly improve the modeling speed of the support vector machine and lower the memory consumption, so that the support vector machine technology can be applied to a big data analysis scene, thereby remedying the defect that a neural network method, a Bayes method and the like in the big data analysis have low prediction accuracy.

Description

technical field [0001] The invention belongs to the technical field of data mining and big data analysis, in particular to a support vector machine modeling method based on data compression. Background technique [0002] With the rapid development of computer technology, especially the continuous application of Internet technology, people's ability to use network information technology to generate and collect data has been greatly improved, and the data has shown a rapid growth trend. How to obtain the required information from massive data has become an urgent research problem. Faced with such a challenge, Data Mining technology emerges at the historic moment, using data mining technology to obtain hidden useful information from these massive data. However, due to the explosive growth of data, how to use data mining technology to quickly and effectively obtain hidden useful information from massive data has become more and more important. Therefore, data mining technology...

AI Technical Summary

Problems solved by technology

Therefore, when the learning samples (number of data records) reach millions of massive data, the storage and calculation of the matrix will consume much more machine memory and computing time than other similar methods. Application of data analysis scenarios

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