A wrapper method for feature selection using Support Vector Machines

TL;DR: The Recursive Feature Elimination algorithm is extended by proposing three approaches to rank variables based on non-linear SVM and SVM for survival analysis, which perform better than the classical RFE of Guyon for realistic scenarios about the structure of biomedical data.

TL;DR: This paper proposes a new unsupervised spectral feature selection model by embedding a graph regularizer into the framework of joint sparse regression for preserving the local structures of data by proposing a novel joint graph sparse coding (JGSC) model.

TL;DR: This work presents a comprehensive overview of various feature selection methods and their inherent pros and cons, and analyzes adaptive classification systems and parallel classification systems for chronic disease prediction.

TL;DR: This hybrid algorithm, called maximum relevance minimum redundancy PSO (mr^2PSO), is novel in the sense that it uses the mutual information available from the filter model to weigh the bit selection probabilities in the discrete PSO.

TL;DR: The contributions of this special issue cover a wide range of aspects of variable selection: providing a better definition of the objective function, feature construction, feature ranking, multivariate feature selection, efficient search methods, and feature validity assessment methods.

TL;DR: This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory, and will guide practitioners to updated literature, new applications, and on-line software.

TL;DR: Learning with Kernels provides an introduction to SVMs and related kernel methods that provide all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms.

TL;DR: In this article, a Support Vector Machine (SVM) method based on recursive feature elimination (RFE) was proposed to select a small subset of genes from broad patterns of gene expression data, recorded on DNA micro-arrays.