Feature Selection Applied to Microarray Data.
Methods Mol Biol 2019;
1986:123-152. [PMID:
31115887 DOI:
10.1007/978-1-4939-9442-7_6]
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Abstract
A typical characteristic of microarray data is that it has a very high number of features (in the order of thousands) while the number of examples is usually less than 100. In the context of microarray classification, this poses a challenge for machine learning methods, which can suffer overfitting and thus degradation in their performance. A common solution is to apply a dimensionality reduction technique before classification, to reduce the number of features. This chapter will be focused on one of the most famous dimensionality reduction techniques: feature selection. We will see how feature selection can help improve the classification accuracy in several microarray data scenarios.
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