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
CN104766098AInactive Publication Date: 2015-07-08HARBIN INST OF TECH
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
HARBIN INST OF TECH
Publication Date
2015-07-08
Estimated Expiration
Not applicable · inactive patent

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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.
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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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