A Survey on Filter Techniques for Feature Selection in Gene Expression Microarray Analysis

A plenitude of feature selection (FS) methods is available in the literature, most of them rising as a need to analyze data of very high dimension, usually hundreds or thousands of variables. Such data sets are now available in various application areas like combinatorial chemistry, text mining, multivariate imaging, or bioinformatics. As a general accepted rule, these methods are grouped in filters, wrappers, and embedded methods. More recently, a new group of methods has been added in the general framework of FS: ensemble techniques. The focus in this survey is on filter feature selection methods for informative feature discovery in gene expression microarray (GEM) analysis, which is also known as…

• TĐ
  Trường Đại học Bách Khoa Hà Nội
• VU
  Vrije Universiteit Brussel
• I
  Innoviris
• UD
  Université de Reims Champagne-Ardenne