Open AccessA Deep Learning-Based Hybrid Feature Selection Approach for Cancer Diagnosis
Author(s) -
Haoran Wu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1848/1/012019
Subject(s) - feature selection , autoencoder , computer science , artificial intelligence , feature learning , machine learning , deep learning , feature (linguistics) , task (project management) , selection (genetic algorithm) , pattern recognition (psychology) , engineering , philosophy , linguistics , systems engineering
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.