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Construction method for classifier

A construction method and classifier technology, applied in the field of data processing, can solve the problems of low classification accuracy of recognition or classification technology, achieve the effect of improving data imbalance, simple method, and improving accuracy

Inactive Publication Date: 2015-07-08
HARBIN INST OF TECH
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  • Summary
  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The present invention provides a method for constructing a classifier, the purpose of which is to solve the problem of low accuracy of the existing identification or classification technology for data such as flow data

Method used

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Embodiment Construction

[0024] In order to make the purpose of the invention, technical solutions and beneficial effects of the present invention clearer, the embodiments of the present invention will be described below in conjunction with the accompanying drawings. The features in can be combined arbitrarily with each other.

[0025] An embodiment of the present invention provides a method for constructing a classifier, the construction method comprising: using the undersampling method to remove part of the majority class training samples in the training sample set, and updating the current training sample with the undersampled training sample set set, wherein the training sample set includes majority class training samples and minority class training samples, and each training sample in the training sample set has a known category; and the minority class training samples in the training sample set are oversampled to utilize the The processed training sample set is used to construct a classifier.

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Abstract

The invention relates to a construction method for a classifier. The construction method includes the following steps that a part of majority class training samples in a training sample set are removed through an undersampling method, and a current training sample set is updated through the undersampled training sample set, wherein the training sample set comprises the majority class training samples and minority class training samples, and the classes of all the training samples in the training sample set are known; oversampling is conducted on the minority class training samples in the training sample set, and the classifier is constructed through the oversampled training sample set. According to the construction method for the classifier, noise in the training samples is removed effectively, the problem of data imbalance can be solved effectively, the accuracy rate of training sample data classification is greatly increased, the calculation amount is small, and the method is simple.

Description

technical field [0001] The invention relates to the field of data processing, in particular to a method for constructing a classifier. Background technique [0002] In the traffic identification method based on machine learning, noise data will appear in the traffic data set, which has a great impact on the identification of small sample data in unbalanced data. Therefore, data cleaning and denoising of network traffic has a great impact on the accuracy of classification has important meaning. For the machine learning method of network traffic classification, there may be a small part of noise traffic and a small part of useful traffic that need to be obtained in traffic identification. [0003] At present, the existing identification or classification technologies for data such as traffic data have low classification accuracy, and the calculations are large, the method is complicated, and it is too time-consuming. Contents of the invention [0004] The present invention...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06K9/66
Inventor 余翔湛叶麟张伟哲何慧张宏莉丛小亮王岳
Owner HARBIN INST OF TECH
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