Name
Abstract | ||
|---|---|---|
Microarray data analysis has emerged as a strong tool for cancer diagnosis. Nevertheless, researches on it are significantly challenging as the microarray datasets are imbalanced and high-dimensional with relatively small sample size. In this paper, we utilized Dual Denoising Autoencoder Features (DDAF), which integrates two Denoising Auto-Encoders (DAE) with different activation function to map the features for both minority and majority classes into a better classification representation. The experimental results on four typical microarray datasets show that the DDAF outperforms the Dual Autoencoder Features (DAF) and the Cost-sensitive Oversampling Stacked Denoising Auto-Encoder (CO-SDAE), rendering the robust ability for dimensionality reduction and imbalanced classification. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICCICC46617.2019.9146039 | 2019 IEEE 18th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) |
Keywords | DocType | ISBN |
imbalanced classification,denoising autoencoder,feature learning | Conference | 978-1-7281-1419-4 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuqing Gao | 1 | 0 | 0.34 |
| Wing W. Y. Ng | 2 | 528 | 56.12 |
| Ting Wang | 3 | 725 | 120.28 |
| Sam Kwong | 4 | 4590 | 315.78 |