Imbalance sample classification method based on PSO (Particle Swarm Optimization) algorithm

Abstract

The invention provides an imbalance sample classification method based on a PSO (Particle Swarm Optimization) algorithm. A PSO algorithm is adopted, the sampling rates of a boundary sample and a safe sample in an oversampling process are optimized to obtain an optimal oversampling multiplying power, and meanwhile, characteristics are optimized so as to select a characteristic combination which can simplify operations and improve a classification result and has best representativeness. The imbalance sample classification method adopts AUC/F-Mea as the fitness function of the algorithm so as to improve the effect of a final classifier.

Description

technical field [0001] The invention belongs to the field of classification technology optimization in data mining, in particular to a classification method applicable to unbalanced samples. Background technique [0002] In the problem of unbalanced sample classification, unbalanced data means that in the sample space of the entire data set, there is a huge gap in the number of samples of one class and the samples of the other class or classes. Often the minority class in this case requires us to devote more attention. For example, in the application of medical diagnosis, the data sample space of cancer or heart disease is an unbalanced sample. In this type of sample, the objects we pay attention to are often diseased samples. By accurately classifying the attributes of these samples, we can accurately Diagnose the patient's condition and give these patients timely and targeted treatment. [0003] In order to pursue a higher global classification accuracy when dealing with...

AI Technical Summary

Problems solved by technology

The weight coefficients of these base classifiers often have an influence or relationship with each other, and the final c

Images