A review of microarray datasets and applied feature selection methods

By Verónica Bolón-Canedo, Noelia Sánchez-Maroño, Amparo Alonso-Betanzos, José Manuel Benítez, Francisco Herrera. Information Sciences, 2014

638 citations recorded by the source corpus.

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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/db662386cae4950e3dff50f82538356b84ab2984

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In Molecular Biology

Citations in the corpus

Recent advances in feature selection and its applications (2017, background): “A comprehensive survey of earlier work can be found in [91,92].”
From explanations to feature selection: assessing SHAP values as feature selection mechanism (2020, background): “Feature selection algorithms are usually classified into three groups: filter, wrapper, and embedded [6], [8], [9].”
Feature selection for high-dimensional data (2016, background): “Due to the complex problem addressed, such data sets have been widely studied in the literature [8].”
Recent advances and emerging challenges of feature selection in the context of big data (2015, methodology): “aforementioned DNA microarray classification [68] and image analysis [96,97].”
Supervised, Unsupervised, and Semi-Supervised Feature Selection: A Review on Gene Selection (2016, background): “Even though there are a lot of review papers on gene selection in the literatures [4], [5], [6], [7], [8], but to the best of our knowledge, there is no detailed discussion on the methods and separates them in such three categories as in this paper.”
Heart Disease Identification Method Using Machine Learning Classification in E-Healthcare (2020, neutral): “predictive and short computationally complex models [30].”
Machine Learning methods for Quantitative Radiomic Biomarkers (2015, methodology): “Various feature selection methods have been employed for high-throughput data mining problems 38 .”
An embedded feature selection method for imbalanced data classification (2019, methodology): “The work [14] presents a new correlation-based feature selection method to identify relevant and redundancy features at the same time.”