A Deep Learning-Based Hybrid Feature Selection Approach for Cancer Diagnosis

Feature selection plays an important role in machine learning-based classification tasks, especially in high dimensional data, such as biological omics datasets. Recent research has begun to explore the use of deep learning to accomplish this task as a step in feature representation. In this research, we developed a deep learning-based hybrid feature selection approach combing Sparse Autoencoder (SAE) and Logistic Regression-Recursive Features Elimination (LR-RFE) and evaluated our method on TCGA miRNA datasets. The results show that our proposed hybrid method achieves a better performance compared to other comparison methods.