Unbalanced data oversampling method based on minority class sample space distribution

A sample space, minority class technology, applied in the fields of electronics, information engineering, and communications, can solve problems such as poor classification results, and achieve the effect of improving classification accuracy and effectiveness
CN113269200AInactive Publication Date: 2021-08-17NANJING UNIV OF INFORMATION SCI & TECH
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Patent Information

Authority / Receiving Office
CN ยท China
Patent Type
Applications(China)
Current Assignee / Owner
NANJING UNIV OF INFORMATION SCI & TECH
Publication Date
2021-08-17
Estimated Expiration
Not applicable ยท inactive patent

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Abstract

The invention discloses an unbalanced data oversampling method based on minority class sample space distribution, and belongs to the technical field of electronics, communication and information engineering. According to the method, the imbalance of a data set is improved by adding noise filtering preprocessing, designing a new sample synthesis method and constructing a calculation rule of a weight value, the problem of poor classification effect caused by a sample aliasing phenomenon is solved, and the performance of an imbalance learning problem is improved.
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Description

Technical field

[0001] The present invention relates to the technical field of electronics, communication, and information engineering, and more particularly to unbalanced data over sampling methods based on minority sample spatial distribution. Background technique

[0002] Unbalanced data is widely used in various real estate, such as financial fraud testing, medical disease diagnosis, network intrusion detection, network fault diagnosis, etc. This data set is called an unbalanced dataset when the number of samples of different categories of samples is varied. Usually, the number of samples is called a number of classes, and the number of samples is small, called a few categories.

[0003] Although these small number of samples have fewer number of samples, the sample data quality is also poor, but it usually carries more important information. And in fact, we pay more attention to the ability of model correctly classified a small number of samples, such as a complex network sy...

Claims

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