Abstract

Aim of study of the study is to effectively solve the imbalanced data sets classification problem, the field of machine learning proposed many effective algorithms. On Seabrook’s view, now the classification methods for class imbalance problem can be broadly divided into two categories, one class is to create new methods or improve the existing methods based on the characteristics of class imbalance. Another is to reduce class imbalance effect re- use of existing methods by resembling technique. But there are also some drawbacks in resembling method. Sample set sampling may lead to excessive learning; the next sample may result in the training set. Therefore, by dividing the training set, it does not increase the number of training samples; also it is without loss of useful information in the sample to obtain a certain degree of balance in a subset of the problem.

Keywords:

Artificial Neural Network, autonomous hybrid power system static var compensator,

References

Competing interests

The authors have no competing interests.

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The authors have no competing interests.