Feature selection may improve deep neural networks for the bioinformatics problems

Fengfeng Zhou

Abstract:

Motivation: Deep neural network (DNN) algorithms were utilized in predicting various biomedical phenotypes recently,
and demonstrated very good prediction performances without selecting features. This study proposed a hypothesis
that the DNN models may be further improved by feature selection algorithms.
Results: A comprehensive comparative study was carried out by evaluating 11 feature selection algorithms on three
conventional DNN algorithms, i.e. convolution neural network (CNN), deep belief network (DBN) and recurrent neural
network (RNN), and three recent DNNs, i.e. MobilenetV2, ShufflenetV2 and Squeezenet. Five binary classification
methylomic datasets were chosen to calculate the prediction performances of CNN/DBN/RNN models using feature
selected by the 11 feature selection algorithms. Seventeen binary classification transcriptome and two multi-class
transcriptome datasets were also utilized to evaluate how the hypothesis may generalize to different data types. The
experimental data supported our hypothesis that feature selection algorithms may improve DNN models, and the
DBN models using features selected by SVM-RFE usually achieved the best prediction accuracies on the five methylomic
datasets.
Availability and implementation: All the algorithms were implemented and tested under the programming environment
Python version 3.6.6.
Contact: [e-mail]
Supplementary information: Supplementary data are available at Bioinformatics online.