Dual autoencoders features for imbalance classification problem

By Wing W. Y. Ng, Guangjun Zeng, Jianjun Zhang, Daniel S. Yeung, Witold Pedrycz. 2016

101 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Media Technology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Nojun Kwak
2. Seonguk Park
3. Jangho Kim
4. Lin Wang
5. Kuk-Jin Yoon
6. Dacheng Tao
7. Jianping Gou
8. S. Maybank

In Electrical and Electronic Engineering

1. Nojun Kwak
2. Seonguk Park
3. Jangho Kim
4. Yudong Zhang
5. Jianqing Fan
6. Lenan Wu
7. Elynn Y. Chen

In Media Technology

1. Nojun Kwak
2. Seonguk Park
3. Jangho Kim

Citations in the corpus

• Paraphrasing Complex Network: Network Compression via Factor Transfer (2018, background): “Many studies show that autoencoder model can learn meaningful, abstract features and thus achieve better classification results in high-dimensional data, such as images (Ng et al., 2016; Shin et al., 2013).”