An imbalanced data classification algorithm of improved autoencoder neural network

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...

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Bibliographic Details
Published in:2016 Eighth International Conference on Advanced Computational Intelligence (ICACI) pp. 95 - 99
Main Authors: Chenggang Zhang, Wei Gao, Jiazhi Song, Jinqing Jiang
Format: Conference Proceeding
Language:English
Published: IEEE 01.02.2016
Subjects:
Biological neural networks
Cancer
classification
Classification algorithms
cost sensitive
imbalanced data
Machine learning algorithms
Noise reduction
oversampling
stacked denoising autoencoder neural network
Training
ISBN:9781467377805, 1467377805
Online Access:Get full text
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Summary: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.
ISBN:9781467377805
1467377805
DOI:10.1109/ICACI.2016.7449810