Modeling method for support vector machine based on data compression

A technology of support vector machine and modeling method, which is applied in the fields of electrical digital data processing, special data processing applications, computer parts, etc. The effect of increasing the number of samples, improving modeling speed, and reducing memory consumption

Inactive Publication Date: 2016-03-02
NAT COMP NETWORK & INFORMATION SECURITY MANAGEMENT CENT +1
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  • Abstract
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  • Application Information

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 m

Method used

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  • Modeling method for support vector machine based on data compression
  • Modeling method for support vector machine based on data compression
  • Modeling method for support vector machine based on data compression

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Embodiment Construction

[0031] Embodiments of the present invention are described in further detail below in conjunction with the accompanying drawings:

[0032] A support vector machine modeling method based on data compression. First, based on the idea of ​​clustering, the data is stored in the clustering feature tree, that is, the data is divided into a series of dissimilar sample groups, and only one of each sample group is selected. Or a limited number of samples represent this group, so as to achieve data compression; then establish a support vector machine model based on the compressed data, so as to shorten the modeling time and memory consumption as much as possible without reducing the accuracy of the model, thereby improving the support vector The data processing capacity of the machine.

[0033] Such as figure 1 As shown, the modeling method of support vector machine based on data compression includes the following steps:

[0034] Step 1. Sampling the modeling data using an equidistant ...

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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...

Claims

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Application Information

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IPC IPC(8): G06F17/30G06K9/62
CPCG06F16/2462G06F18/2411
Inventor 王洋黄瑞陈训逊苏卫卫吴震于文峰蒋旭
Owner NAT COMP NETWORK & INFORMATION SECURITY MANAGEMENT CENT
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