GAN-based semi-supervised for imbalanced data classification
Tingting Zhou, Wei Liu, Congyu Zhou, Leiting Chen · 2018
Most of the traditional classification algorithms are based on the premise that the datasets are uniformly distributed or roughly equivalent. Once the sample dataset is not balanced , the classification performance drops sharply. To efficiently deal with the imbalance of data, an improved generative adversarial network (GAN) algorithm is proposed in this work . Firstly, we construct artificial samples so that more minority-class's data can be obtained via optimizing GAN loss function. Secondly, we build a fully-connected network for structured data classification. Finally, experimental evaluations are conducted on two open structured-datasets and the results of the proposed algorithm demonstrate a good applicability for the classification of structured data.