Hierarchy gravity model based imbalanced data classification method and system therefor

Abstract

The present invention discloses a hierarchy gravity model based imbalanced data classification method and a system therefor. The method comprises the following steps: S1, acquiring a to-be-classified sample set Z, wherein each sample in the to-be-classified sample set Z comprises D attributes, and D is a positive integer; S2, performing interval division on attribute values of samples, dividing each attribute into L intervals, dividing the samples into LD D-dimensional cubes, and calculating an attribute weight, wherein L is a positive integer; and S3, dividing feature space, placing the to-be-classified sample set Z into a corresponding hierarchy D-dimensional cube, and performing label classification on the samples in conjunction with a gravity model. The hierarchy gravity model based imbalanced data classification method and the system therefor have the following advantages: the attributes are weighted at different resolutions for a multi-hierarchy model, and classification performance of a hierarchy model is improved; classification efficiency is improved by dividing the attributes at different resolutions and establishing a hierarchy classification model; and classification precision of data in a space overlapping area is optimized by using a partial gravity model.

Description

technical field [0001] The invention relates to the field of computer data analysis and mining, in particular to an unbalanced data classification method and system based on a hierarchical gravity model. Background technique [0002] An unbalanced data set is a data set in which there is a large difference between the number of samples owned by each class. In the binary classification of unbalanced data sets, the class with a small number of samples is usually called a positive class, and the corresponding class with a large number of samples is called a negative class. Data imbalance is very common in current applications, such as medical diagnosis, intrusion detection, fraud prevention, and classification of things from satellite images. The correct rate of positive classification is our main concern. For example, in disease diagnosis, Misdiagnosis of healthy people will be resolved during reexamination, but cancer patients may be misdiagnosed as normal, which may cause i...

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Problems solved by technology

However, this method affects the calculation accuracy of the data gravitational field, especially near the centroid of the data particle, because in the neighborhood of the data particle centroid, due to the relatively dense original data, the gradient of the data gravitational field in this area changes quickly, and the field comparison complex, and after the data particles are created, the data gravitational field calculated based on the data particles loses some information of the original gravitational field, so this will inevitably affect the classification accuracy

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