An embedded feature selection method based on generalized classifier neural network for cancer classification

The selection of relevant genes plays a vital role in classifying high-dimensional microarray gene expression data. Sparse group Lasso and its variants have been employed for gene selection to capture the interactions of genes within a group. Most of the embedded methods are linear sparse learning models that fail to capture the non-linear interactions. Additionally, very less attention is given to solving multi-class problems. The existing methods create overlapping groups, which further increases dimensionality. The paper proposes a neural network-based embedded feature selection method that can represent the non-linear relationship. In an effort toward an explainable model, a generalized classifier neural network (GCNN) is adopted as the model for the proposed embedded feature selection. GCNN has well-defined architecture in terms of the number of layers and neurons within each layer. Each layer has a distinct functionality, eliminating the obscure nature of most neural networks. The paper proposes a feature selection approach called Weighted GCNN (WGCNN) that embeds feature weighting as a part of training the neural network. Since the gene expression data comprises a large number of features, to avoid overfitting of the model a statistical guided dropout is implemented at the input layer. The proposed method works for binary as well as multi-class classification problems likewise. Experimental validation is carried out on seven microarray datasets on three learning models and compared with six state-of-art methods that are popularly employed for feature selection. The WGCNN performs well in terms of the F1 score and the number of features selected.

Automated summary

Gene selection is crucial for classifying high-dimensional microarray gene expression data. This paper proposes a neural network-based embedded feature selection method called Weighted GCNN (WGCNN), which can capture non-linear interactions and solve multi-class problems. The WGCNN incorporates feature weighting and statistical guided dropout to avoid overfitting. Experimental validation demonstrates that the WGCNN performs well in terms of F1 score and number of features selected.