A meta-review of feature selection techniques in the context of microarray data

Mungloo-Dilmohamud, Zahra (University of Mauritius, Reduit, Mauritius) ; Jaufeerally-Fakim, Yasmina (University of Mauritius, Reduit, Mauritius) ; Peña-Reyes, Carlos Andrés (School of Management and Engineering Vaud, HES-SO // University of Applied Sciences Western Switzerland)

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.


Keywords:
Conference Type:
full paper
Faculty:
Ingénierie et Architecture
School:
HEIG-VD
Institute:
IICT - Institut des Technologies de l'Information et de la Communication
Publisher:
Cham, Springer
Date:
2017-04
Cham
Springer
Pagination:
17 p.
Published in:
Lecture Notes in Computer Science ; Proceedings of International Conference on Bioinformatics and Biomedical engineering (IWBBIO 2017), Bioinformatics and Biomedical Engineering, 26-28 April 2017, Granada, Spain
DOI:
ISSN:
0302-9743
ISBN:
978-3-319-56147-9
Appears in Collection:



 Record created 2020-01-31, last modified 2020-02-11


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