A Meta-Review of Feature Selection Techniques in the Context of Microarray Data

• Published in: Bioinformatics and Biomedical Engineering Vol. 10208; pp. 33 - 49
• Main Authors: Mungloo-Dilmohamud, Zahra, Jaufeerally-Fakim, Yasmina, Peña-Reyes, Carlos
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2017; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Feature selection; Machine learning; Microarray data; Statistical methods
• ISBN: 3319561472; 9783319561479
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-56148-6_3

Microarray technologies produce very large amounts of data that need to be classified for interpretation. Large data coupled with small sample sizes make it challenging for researchers to get useful information and therefore a lot of effort goes into the design and testing of feature selection tools; literature abounds with description of numerous methods. In this paper we select five representative review papers in the field of feature selection for microarray data in order to understand their underlying classification of methods. Finally, on this base, we propose an extended taxonomy for categorizing feature selection techniques and use it to classify the main methods presented in the selected reviews.