A Review of Feature Selection and Feature Extraction Methods Applied on Microarray Data
By Zena M. Hira, Duncan Fyfe Gillies. Adv. Bioinformatics, 2015
904 citations recorded by the source corpus.
We summarise various ways of performing dimensionality reduction on high-dimensional microarray data. Many different feature selection and feature extraction methods exist and they are being widely used. All these methods aim to remove redundant and irrelevant features so that classification of new instances will be more accurate. A popular source of data is microarrays, a biological platform for gathering gene expressions. Analysing microarrays can be difficult due to the size of the data they provide. In addition the complicated relations among the different genes make analysis more difficult and removing excess features can improve the quality of the results. We present some of the most popular methods for selecting significant features and provide a comparison between them. Their advantages and disadvantages are outlined in order to provide a clearer idea of when to use each one of them for saving computational time and resources.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/88cf613b8eccf597eef922c2f70c2d34399306e0
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In Molecular Biology
Citations in the corpus
- Survey on supervised machine learning techniques for automatic text classification (2019, methodology): “According to Hira and Gillies (2015), a word co-occurrence distribution technique using a clustering method for extracting keywords for a single document without based on a huge dataset, and established hopeful findings.”
- Machine Learning and Integrative Analysis of Biomedical Big Data (2019, methodology): “DR techniques are employed either as feature extraction (FE) or feature selection (FS) [35,36].”
- A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction (2020, background): “Feature extraction algorithms are classified into linear and nonlinear algorithms [39, 43, 45].”