Data equalization method, system and equipment

Abstract

The invention discloses a data equalization method. The method comprises the following steps: receiving an input non-equalization data set; calculating a boundary judgment factor of each sample in theminority class sample set; sampling the boundary set of the plurality of types of sample sets through a sparse sampling strategy to obtain a new plurality of types of sample sets; carrying out interpolation through a partition neighborhood interpolation strategy, and constructing a new minority class sample set; and combining the new majority class sample set with the new minority class sample set to obtain a processed unbalanced data set. According to the invention, the decision space of a plurality of types of boundary samples is shrunk, and the sample boundaries of a plurality of types anda few types become clearer; and meanwhile, through a partition neighborhood interpolation strategy, the sample boundary becomes clearer, and the mining effect of the unbalanced data is improved. Theinvention further provides a data equalization system and equipment and a computer readable storage medium which have the above beneficial effects.

Description

technical field [0001] The present application relates to the field of data mining, in particular to a data equalization method, system, device and computer-readable storage medium. Background technique [0002] Unbalanced data mining has become one of the most challenging problems in the field of data analysis. There are a large amount of unbalanced data in practical applications, such as hospital patient diagnosis data, network intrusion data, telecom fraud data, etc., and the minority samples are usually It contains important information and is an important goal of data mining. [0003] At present, research on the classification of unbalanced data is mainly carried out from two levels of data and algorithms. At the algorithm level, improve the model’s ability to handle unbalanced datasets by optimizing traditional classification algorithms or proposing new classification algorithms, such as active learning, ensemble learning methods, single-class learning methods, and co...

AI Technical Summary

Problems solved by technology

However, doing so will easily lead to the loss of important sample information, destroy the distribution characteristics of the original data set, etc., thus making the mining effect of unbalanced data unsatisfactory.

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