An imbalanced data classification algorithm of improved autoencoder neural network
Chenggang Zhang, Wei Dong Gao, Jiazhi Song, Jinqing Jiang · 2016
Imbalanced data classification problem has always been a hotspot in the field of machine learning research. Pointing to the overfitting and noise problems of oversampling algorithm when synthesizing new minority class samples, the current study proposed a stacked denoising autoencoder neural network (SDAE) algorithm based on cost-sensitive oversampling, combining the cost-sensitive learning with denoising autoencoder neural network. The proposed algorithm can not only oversample minority class sample through misclassification cost, but it can denoise and classify the sampled dataset. Experiment shows that, compared with the traditional stacked autoencoder neural network (SAE) and oversampling autoencoder neural network without denoising process (OS-SAE), the proposed algorithm improves the classification accuracy of minority class of imbalanced datasets.