The DOI matches its Crossref record; 129 of the 129 references it cites were found in a registry or an index.
Supervised, Unsupervised, and Semi-Supervised Feature Selection: A Review on Gene Selection
Abstract
Recently, feature selection and dimensionality reduction have become fundamental tools for many data mining tasks, especially for processing high-dimensional data such as gene expression microarray data. Gene expression microarray data comprises up to hundreds of thousands of features with relatively small sample size. Because learning algorithms usually do not work well with this kind of data, a challenge to reduce the data dimensionality arises. A huge number of gene selection are applied to select a subset of relevant features for model construction and to seek for better cancer classification performance. This paper presents the basic taxonomy of feature selection, and also reviews the state-of-the-art gene selection methods by grouping the literatures into three categories: supervised, unsupervised, and semi-supervised. The comparison of experimental results on top 5 representative gene expression datasets indicates that the classification accuracy of unsupervised and semi-supervised feature selection is competitive with supervised feature selection.
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Li, J., Cheng, K., Wang, S., Morstatter, F., Trevino, R. P., Tang, J., & Liu, H. (2018) · ACM Computing Surveys · doi:10.1145/3136625 · Cited by 2,442
From explanations to feature selection: assessing SHAP values as feature selection mechanism
Marcilio, W. E., & Eler, D. M. (2020) · 2020 33rd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) · doi:10.1109/sibgrapi51738.2020.00053 · Cited by 504
Gruson, D., Helleputte, T., Rousseau, P., & Gruson, D. (2019) · Clinical Biochemistry · doi:10.1016/j.clinbiochem.2019.04.013 · Cited by 125
Chemometrics for Raman Spectroscopy Harmonization
Barton, B., Thomson, J., Lozano Diz, E., & Portela, R. (2022) · Applied Spectroscopy · doi:10.1177/00037028221094070 · Cited by 79
Feature Selection Boosted by Unselected Features
Zheng, W., Chen, S., Fu, Z., Zhu, F., Yan, H., & Yang, J. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3058172 · Cited by 51
Metadata
- Authors
- Ang, J. C., Mirzal, A., Haron, H., & Hamed, H. N. A.
- Venue
- IEEE/ACM Transactions on Computational Biology and Bioinformatics
- Published
- 2016-09-01
- Volume
- 13
- Issue
- 5
- Pages
- 971-989
- ISSN
- 1545-5963 (ISSN-L)
- OpenAlex
- W2344681634
- Cited by
- 641
- References
- 129
- FWCI
- 9.26
- Type
- journal article
- Language
- en
- Open access
- closed
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- Ang, J. C., Mirzal, A., Haron, H., & Hamed, H. N. A. (2016). Supervised, Unsupervised, and Semi-Supervised Feature Selection: A Review on Gene Selection. IEEE/ACM Transactions on Computational Biology and Bioinformatics. https://doi.org/10.1109/tcbb.2015.2478454
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