The DOI matches its Crossref record; 110 of the 110 references it cites were found in a registry or an index.
A review of microarray datasets and applied feature selection methods
Abstract
Microarray data classification is a difficult challenge for machine learning researchers due to its high number of features and the small sample sizes. Feature selection has been soon considered a de facto standard in this field since its introduction, and a huge number of feature selection methods were utilized trying to reduce the input dimensionality while improving the classification performance. This paper is devoted to reviewing the most up-to-date feature selection methods developed in this field and the microarray databases most frequently used in the literature. We also make the interested reader aware of the problematic of data characteristics in this domain, such as the imbalance of the data, their complexity, or the so-called dataset shift. Finally, an experimental evaluation on the most representative datasets using well-known feature selection methods is presented, bearing in mind that the aim is not to provide the best feature selection method, but to facilitate their comparative study by the research community.
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Machine Learning methods for Quantitative Radiomic Biomarkers
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A review of feature selection methods in medical applications
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Supervised, Unsupervised, and Semi-Supervised Feature Selection: A Review on Gene Selection
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Ensembles for feature selection: A review and future trends
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Self-citation
Metadata
- Authors
- Bolón-Canedo, V., Sánchez-Maroño, N., Alonso-Betanzos, A., Benítez, J. M., & Herrera, F.
- Venue
- Information Sciences
- Publisher
- Elsevier BV
- Published
- 2014-10
- Volume
- 282
- Pages
- 111-135
- ISSN
- 0020-0255 (ISSN-L)
- OpenAlex
- W2142594886
- Cited by
- 666
- References
- 110
- FWCI
- 17.28
- Type
- journal article
- Language
- en
- Open access
- closed
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