Label noise detection method based on multi-granularity relative density

A relative density and noise detection technology, applied in the direction of instruments, character and pattern recognition, computer components, etc., can solve the problems of not making full use of the noise contrast characteristics of labels, poor information quality, and not much available information, etc., to reduce The risk of overfitting, the effect of reducing time overhead and good generalization ability
CN111178387AInactive Publication Date: 2020-05-19CHONGQING UNIV OF POSTS & TELECOMM
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
CHONGQING UNIV OF POSTS & TELECOMM
Publication Date
2020-05-19
Estimated Expiration
Not applicable · inactive patent

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Abstract

The invention discloses a label noise detection method based on multi-granularity relative density, and belongs to the field of data classification. The method comprises the following steps: S1, dividing a data set into K clusters by utilizing a KMeans algorithm according to a label noise detection method based on multi-granularity relative density, and calculating the improved relative density ofeach sample in granularity; wherein the improved relative density is defined as follows: firstly, respectively calculating mass centers of the positive sample and the negative sample, then solving distances from the samples to the mass centers of the same kind and the mass centers of the different kinds, and taking a ratio of the distances as the improved relative density under the granularity; s2, changing the K value, repeating the process in the step S1, and calculating the improved relative density of each sample under different granularities; and S3, taking a sample of which the improvedrelative density exceeds a certain threshold value as label noise. According to the method, particle size calculation is introduced into the improved relative density model, and the method has higherefficiency than a traditional method.
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Description

technical field

[0001] The invention relates to a label noise detection method based on multi-granularity relative density, which belongs to the field of data classification. Background technique

[0002] Real-world data is always flawed, and the appearance of noisy data is the result of this flaw. Noise processing is an important task in machine learning. In classification problems, noise is mainly divided into two categories: attribute noise and label noise. Attribute noise is caused by errors in the process of inputting attributes, while label noise is caused by label pollution. In general, label noise may be more harmful than attribute noise. First, a sample may have multiple features, yet only one label exists. Second, while each feature has its unique importance, labels always have a greater impact on learning. The performance of the classifier is degraded by the presence of label noise, and the complexity of the model is also increased. In addition, there is also...

Claims

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