Latent regularization for feature selection using kernel methods in tumor classification
This work addresses the challenge of identifying critical biomarkers for cancer diagnosis, offering a domain-specific improvement in feature selection for medical applications.
The authors tackled the problem of selecting key genes for tumor classification from high-dimensional transcriptomics data by proposing a feature selection method using Multiple Kernel Learning with latent regularization, which improved classification performance on unseen test samples compared to other supervised approaches.
The transcriptomics of cancer tumors are characterized with tens of thousands of gene expression features. Patient prognosis or tumor stage can be assessed by machine learning techniques like supervised classification tasks given a gene expression profile. Feature selection is a useful approach to select the key genes which helps to classify tumors. In this work we propose a feature selection method based on Multiple Kernel Learning that results in a reduced subset of genes and a custom kernel that improves the classification performance when used in support vector classification. During the feature selection process this method performs a novel latent regularisation by relaxing the supervised target problem by introducing unsupervised structure obtained from the latent space learned by a non linear dimensionality reduction model. An improvement of the generalization capacity is obtained and assessed by the tumor classification performance on new unseen test samples when the classifier is trained with the features selected by the proposed method in comparison with other supervised feature selection approaches.