Method for extracting sensitive data from unbalanced data based on SVM-forest

Abstract

The invention discloses a method for extracting sensitive data from unbalanced data based on SVM forest. The method comprises the steps that a part of labeled samples are taken as test samples, and the rest of the samples are used as training samples; k-Means is used to divide a normal working condition class into subclasses, and the subclasses are mixed with fault working condition type data to form N training subsets; an SVM-tree method is used to train SVM-Forest, and the test samples are used to test the SVM-forest; L trees with the highest fault working condition misclassification rate are selected; some data with a great influence on the classification effect are kept; according to a selection classification algorithm, a classifier T is trained through the minority classes and the remaining majority classes in a test set; and a temporary test sample is used to test the classification effect of T until the effect meets requirements. According to the sensitive data extracting method provided by the invention, samples with a great influence on the classification effect in a majority of sample sets are selected through multiple iterations to reduce the degree of unbalance; and the classification effect is close to or up to an equal classification effect under the same condition.

Description

technical field [0001] The invention belongs to the field of industrial process control, in particular to a method for extracting sensitive data from unbalanced data based on SVM-forest. Background technique [0002] In the work of industrial fault classification, some commonly used classification methods have a prerequisite for use, that is, the amount of data in the training set is equivalent. But the reality is often not the case. When a certain type of data is very large, or a certain type of data is rare, that is, unbalanced data appears, directly using the traditional classification method will produce a large classification error. Contents of the invention [0003] Aiming at the deficiencies of the prior art, the present invention proposes a method for extracting sensitive data from unbalanced data based on SVM-forest. This method mainly improves the traditional classification method at the sampling level, and selects most sample sets through multiple iterations. S...

AI Technical Summary

Problems solved by technology

But the reality is often not the case. When a certain type of data is very large, or a certain type of data is rare, that is, unbalanced data appears, directly using the traditional classification method will cause a large classification error.

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