Recurrent Neural Network Based Feature Selection for High Dimensional and Low Sample Size Micro-array Data

Chowdhury S, Dong X, Li X 2019. IEEE BigData 2019


Analyzing micro-array data faces many challenges such as high dimension, low sample size and sparse data. Feature selection is a technique to select more relevant features to implement dimension reduction to mitigate these challenges. In this paper, we propose a novel framework of feature selection based on recurrent neural network (RNN) to select a subset of features. Specifically, the proposed framework has been applied to select features from micro-array data for cell classification. We implement four feature selection models with different architectures of recurrent neural networks under the proposed framework, where these architectures include gated recurrent unit (GRU), long short-term memory (LSTM), RNN and bi- directional LSTM. The advantages of the proposed framework is demonstrated via real-world micro-array datasets.